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First fully ML-framework-free 3D Gaussian Splatting implementation in LichtFeld Studio. I’ve completed the migration of the full training pipeline to a custom CUDA-based tensor library. No PyTorch, no LibTorch, no autograd. Every gradient is implemented by hand, either through CUDA kernels or minimal abstractions on top. This makes it...

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MrNeRF

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50,539 görüntüleme • 8 ay önce •via X (Twitter)

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Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.
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Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.

KIRI Engine - 3D Scanner App

12,125 görüntüleme • 1 ay önce

In the actual account of meeting Brig ML Khetarpal and Brig Naseer, there is no cowardice, there is no repentance, there is no anti-war sentiment. Only Soldierly pride and mutual respect for professionalism. Brig ML Khetarpal was a third generation soldier. He could have never said things like, why his son did not yield (in the face of enemy fire). This fictionalized account is a disgrace to the memory of the paramveer Arun Khterpal and a disservice to his father Brig ML Khetarpal. Today, Brig Khetarpal is not there to refute this mockery, but are we all dead to accept this farce?
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In the actual account of meeting Brig ML Khetarpal and Brig Naseer, there is no cowardice, there is no repentance, there is no anti-war sentiment. Only Soldierly pride and mutual respect for professionalism. Brig ML Khetarpal was a third generation soldier. He could have never said things like, why his son did not yield (in the face of enemy fire). This fictionalized account is a disgrace to the memory of the paramveer Arun Khterpal and a disservice to his father Brig ML Khetarpal. Today, Brig Khetarpal is not there to refute this mockery, but are we all dead to accept this farce?

We, the people of India

82,415 görüntüleme • 6 ay önce

Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."
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Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."

MrNeRF

17,206 görüntüleme • 1 yıl önce

F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Consistent Gaussian Splatting Contributions: • We pioneer 3D-aware generation using generalizable feed-forward Gaussian Splatting representation, achieving significant efficiency and favorable rendering quality on monocular datasets. • We significantly advance the capability of pixel-aligned Gaussian Splatting representations by designing a self-supervised cycle training strategy specifically tailored for monocular datasets. • We further mitigate the artifacts of 3D-aware representations caused by large viewpoint shifts by introducing geometry-aware video priors.
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F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Consistent Gaussian Splatting Contributions: • We pioneer 3D-aware generation using generalizable feed-forward Gaussian Splatting representation, achieving significant efficiency and favorable rendering quality on monocular datasets. • We significantly advance the capability of pixel-aligned Gaussian Splatting representations by designing a self-supervised cycle training strategy specifically tailored for monocular datasets. • We further mitigate the artifacts of 3D-aware representations caused by large viewpoint shifts by introducing geometry-aware video priors.

MrNeRF

14,229 görüntüleme • 1 yıl önce

MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting Contributions: • MoE-GS: the first dynamic Gaussian splatting framework employing a Mixture-of-Experts architecture, enabling robust and adaptive reconstruction across diverse dynamic scenes. • A novel Volume-aware Pixel Router integrates expert outputs through differentiable weight splatting, achieving spatially and temporally coherent adaptive blending. • Efficiency of MoE-GS is improved through single-pass multi-expert rendering and gate-aware Gaussian pruning. A separate knowledge distillation strategy trains individual experts with pseudo-labels from the MoE model, enhancing quality without modifying the architecture.
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MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting Contributions: • MoE-GS: the first dynamic Gaussian splatting framework employing a Mixture-of-Experts architecture, enabling robust and adaptive reconstruction across diverse dynamic scenes. • A novel Volume-aware Pixel Router integrates expert outputs through differentiable weight splatting, achieving spatially and temporally coherent adaptive blending. • Efficiency of MoE-GS is improved through single-pass multi-expert rendering and gate-aware Gaussian pruning. A separate knowledge distillation strategy trains individual experts with pseudo-labels from the MoE model, enhancing quality without modifying the architecture.

MrNeRF

10,346 görüntüleme • 8 ay önce

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.
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Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

289,948 görüntüleme • 8 ay önce

Segment Any 3D Gaussians paper page: Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation. However, existing methods face challenges in either achieving fine-grained, multi-granularity segmentation or contending with substantial computational overhead, inhibiting real-time interaction. In this paper, we introduce Segment Any 3D GAussians (SAGA), a novel 3D interactive segmentation approach that seamlessly blends a 2D segmentation foundation model with 3D Gaussian Splatting (3DGS), a recent breakthrough of radiance fields. SAGA efficiently embeds multi-granularity 2D segmentation results generated by the segmentation foundation model into 3D Gaussian point features through well-designed contrastive training. Evaluation on existing benchmarks demonstrates that SAGA can achieve competitive performance with state-of-the-art methods. Moreover, SAGA achieves multi-granularity segmentation and accommodates various prompts, including points, scribbles, and 2D masks. Notably, SAGA can finish the 3D segmentation within milliseconds, achieving nearly 1000x acceleration compared to previous SOTA.
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Segment Any 3D Gaussians paper page: Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation. However, existing methods face challenges in either achieving fine-grained, multi-granularity segmentation or contending with substantial computational overhead, inhibiting real-time interaction. In this paper, we introduce Segment Any 3D GAussians (SAGA), a novel 3D interactive segmentation approach that seamlessly blends a 2D segmentation foundation model with 3D Gaussian Splatting (3DGS), a recent breakthrough of radiance fields. SAGA efficiently embeds multi-granularity 2D segmentation results generated by the segmentation foundation model into 3D Gaussian point features through well-designed contrastive training. Evaluation on existing benchmarks demonstrates that SAGA can achieve competitive performance with state-of-the-art methods. Moreover, SAGA achieves multi-granularity segmentation and accommodates various prompts, including points, scribbles, and 2D masks. Notably, SAGA can finish the 3D segmentation within milliseconds, achieving nearly 1000x acceleration compared to previous SOTA.

AK

69,542 görüntüleme • 2 yıl önce

FastMap: Revisiting Dense and Scalable Structure from Motion "FASTMAP, a redesigned SfM framework, achieves fast, high-accuracy dense structure from motion. On large scenes with thousands of images, FASTMAP is up to one to two orders of magnitude faster than GLOMAP and COLMAP. ... Importantly, FASTMAP achieves efficiency improvements while keeping comparable performance. Extensive experiments on eight datasets demonstrate pose estimation accuracy and novel view synthesis quality close to GLOMAP and COLMAP. " Contributions: 1. For all the iterative nonlinear optimization problems involved, we design algorithms such that the computational complexity of each iteration is only linear in the number of image pairs, not keypoint pairs or 3D points. This includes replacing the traditional bundle adjustment [50] present in previous SfM frameworks with a novel re-weighting epipolar adjustment algorithm, which is much more efficient. 2. Throughout the entire framework, we formulate as many steps as possible as GPU-friendly dense tensor operations. This allows us to implement the entire method in PyTorch [39], which provides seamless GPU acceleration.
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FastMap: Revisiting Dense and Scalable Structure from Motion "FASTMAP, a redesigned SfM framework, achieves fast, high-accuracy dense structure from motion. On large scenes with thousands of images, FASTMAP is up to one to two orders of magnitude faster than GLOMAP and COLMAP. ... Importantly, FASTMAP achieves efficiency improvements while keeping comparable performance. Extensive experiments on eight datasets demonstrate pose estimation accuracy and novel view synthesis quality close to GLOMAP and COLMAP. " Contributions: 1. For all the iterative nonlinear optimization problems involved, we design algorithms such that the computational complexity of each iteration is only linear in the number of image pairs, not keypoint pairs or 3D points. This includes replacing the traditional bundle adjustment [50] present in previous SfM frameworks with a novel re-weighting epipolar adjustment algorithm, which is much more efficient. 2. Throughout the entire framework, we formulate as many steps as possible as GPU-friendly dense tensor operations. This allows us to implement the entire method in PyTorch [39], which provides seamless GPU acceleration.

MrNeRF

15,233 görüntüleme • 1 yıl önce

🚨 Anthropic committed up to 1M TPU chips for Claude. Openai is leasing TPUs for chatgpt inference. Here's How kernels work on TPUs (deep dive 2/6 by emi) pallas is Google's answer to kernel writing. a python kernel SDK built on JAX. still very experimental (jax.experimental.pallas). on TPU it compiles through mosaic; on GPU it lowers to triton. if you know CUDA, the syntax will feel familiar but the execution model is completely different. in CUDA, grid=(4,4) launches 16 blocks running simultaneously across SMs. in pallas, those 16 iterations run one after another in lexicographic order. no threads. no warps. no blocks. no occupancy tuning. a TPU is a sequential machine with a very wide vector register — more like a CPU than a GPU. performance comes from width: a 128x128 systolic array doing matmul and an 8x128 SIMD vector unit doing everything else. maximum parallelism on chip: 2, one per TensorCore in megacore mode. three concepts replace CUDA's thread/block/grid hierarchy. Refs are mutable memory references. because execution is sequential, each iteration safely accumulates without atomics. in CUDA you'd need atomics or a separate reduction pass. the memory model is also very different from NVIDIA's. zero hardware caches. VMEM is 32-128 MiB of software-managed scratchpad — 500-1000x larger than GPU shared memory per SM. all data must be explicitly DMA'd from HBM to VMEM before any computation touches it. four levels: HBM → VMEM → VREGs → MXU/VPU, plus SMEM for scalar control data. every byte of data movement is your responsibility. this is like CUDA shared memory except it's 500x bigger and there's no cache fallback. pipelining is mandatory. without double-buffering HBM→VMEM transfers, the MXU just stalls waiting for data. this is the single most important optimization on TPU. and because grid execution is sequential and deterministic, consecutive iterations that need the same input block skip the redundant HBM transfer automatically, impossible on GPU where block execution order is undefined. the compilation pipeline is unlike anything in this series: python → jaxpr → stableHLO → XLA HLO (71+ optimization passes) → LLO (78+ passes) → 322-bit VLIW bundles. the compiler packs instructions for scalar, vector, matrix, and DMA units into a single 322-bit word. everything in that bundle executes in parallel, with no runtime scheduling.
0:18

🚨 Anthropic committed up to 1M TPU chips for Claude. Openai is leasing TPUs for chatgpt inference. Here's How kernels work on TPUs (deep dive 2/6 by emi) pallas is Google's answer to kernel writing. a python kernel SDK built on JAX. still very experimental (jax.experimental.pallas). on TPU it compiles through mosaic; on GPU it lowers to triton. if you know CUDA, the syntax will feel familiar but the execution model is completely different. in CUDA, grid=(4,4) launches 16 blocks running simultaneously across SMs. in pallas, those 16 iterations run one after another in lexicographic order. no threads. no warps. no blocks. no occupancy tuning. a TPU is a sequential machine with a very wide vector register — more like a CPU than a GPU. performance comes from width: a 128x128 systolic array doing matmul and an 8x128 SIMD vector unit doing everything else. maximum parallelism on chip: 2, one per TensorCore in megacore mode. three concepts replace CUDA's thread/block/grid hierarchy. Refs are mutable memory references. because execution is sequential, each iteration safely accumulates without atomics. in CUDA you'd need atomics or a separate reduction pass. the memory model is also very different from NVIDIA's. zero hardware caches. VMEM is 32-128 MiB of software-managed scratchpad — 500-1000x larger than GPU shared memory per SM. all data must be explicitly DMA'd from HBM to VMEM before any computation touches it. four levels: HBM → VMEM → VREGs → MXU/VPU, plus SMEM for scalar control data. every byte of data movement is your responsibility. this is like CUDA shared memory except it's 500x bigger and there's no cache fallback. pipelining is mandatory. without double-buffering HBM→VMEM transfers, the MXU just stalls waiting for data. this is the single most important optimization on TPU. and because grid execution is sequential and deterministic, consecutive iterations that need the same input block skip the redundant HBM transfer automatically, impossible on GPU where block execution order is undefined. the compilation pipeline is unlike anything in this series: python → jaxpr → stableHLO → XLA HLO (71+ optimization passes) → LLO (78+ passes) → 322-bit VLIW bundles. the compiler packs instructions for scalar, vector, matrix, and DMA units into a single 322-bit word. everything in that bundle executes in parallel, with no runtime scheduling.

wafer

32,409 görüntüleme • 4 gün önce

In a healthy adult male, the bladder doesn’t have a fixed size, it’s a dynamic organ. Most people start feeling the first urge to urinate around 150–250 mL, a stronger urge around 300–400 mL, and the usual functional capacity is roughly 400–600 mL. It can stretch beyond that, sometimes up to 700–800 mL, but that’s uncomfortable and not considered normal everyday function. Now, once a urinary catheter is in place, the physiology changes. The bladder is no longer “storing” urine the way it normally does. Instead, urine drains continuously through the catheter into the collection bag, so the bladder usually stays relatively empty. That’s why in catheterized patients, what you see accumulating is not bladder capacity, it’s just the total urine output over time. As for the urine bags, standard ones typically hold around 1500–2000 mL, which is accurate and designed to safely collect urine over several hours. So overall, the concept is right, just important to remember that with a catheter, you’re no longer measuring how much the bladder can hold, but how much the kidneys are producing.
0:10

In a healthy adult male, the bladder doesn’t have a fixed size, it’s a dynamic organ. Most people start feeling the first urge to urinate around 150–250 mL, a stronger urge around 300–400 mL, and the usual functional capacity is roughly 400–600 mL. It can stretch beyond that, sometimes up to 700–800 mL, but that’s uncomfortable and not considered normal everyday function. Now, once a urinary catheter is in place, the physiology changes. The bladder is no longer “storing” urine the way it normally does. Instead, urine drains continuously through the catheter into the collection bag, so the bladder usually stays relatively empty. That’s why in catheterized patients, what you see accumulating is not bladder capacity, it’s just the total urine output over time. As for the urine bags, standard ones typically hold around 1500–2000 mL, which is accurate and designed to safely collect urine over several hours. So overall, the concept is right, just important to remember that with a catheter, you’re no longer measuring how much the bladder can hold, but how much the kidneys are producing.

Op. Dr. Mehmet Bekir Şen

41,981 görüntüleme • 1 ay önce

In a healthy adult male, the bladder doesn’t have a fixed size, it’s a dynamic organ. Most people start feeling the first urge to urinate around 150–250 mL, a stronger urge around 300–400 mL, and the usual functional capacity is roughly 400–600 mL. It can stretch beyond that, sometimes up to 700–800 mL, but that’s uncomfortable and not considered normal everyday function. Now, once a urinary catheter is in place, the physiology changes. The bladder is no longer “storing” urine the way it normally does. Instead, urine drains continuously through the catheter into the collection bag, so the bladder usually stays relatively empty. That’s why in catheterized patients, what you see accumulating is not bladder capacity, it’s just the total urine output over time. As for the urine bags, standard ones typically hold around 1500–2000 mL, which is accurate and designed to safely collect urine over several hours. So overall, the concept is right, just important to remember that with a catheter, you’re no longer measuring how much the bladder can hold, but how much the kidneys are producing.
0:10

In a healthy adult male, the bladder doesn’t have a fixed size, it’s a dynamic organ. Most people start feeling the first urge to urinate around 150–250 mL, a stronger urge around 300–400 mL, and the usual functional capacity is roughly 400–600 mL. It can stretch beyond that, sometimes up to 700–800 mL, but that’s uncomfortable and not considered normal everyday function. Now, once a urinary catheter is in place, the physiology changes. The bladder is no longer “storing” urine the way it normally does. Instead, urine drains continuously through the catheter into the collection bag, so the bladder usually stays relatively empty. That’s why in catheterized patients, what you see accumulating is not bladder capacity, it’s just the total urine output over time. As for the urine bags, standard ones typically hold around 1500–2000 mL, which is accurate and designed to safely collect urine over several hours. So overall, the concept is right, just important to remember that with a catheter, you’re no longer measuring how much the bladder can hold, but how much the kidneys are producing.

Op. Dr. Mehmet Bekir Şen

34,335 görüntüleme • 2 ay önce

More coming soon ! I manage to integrate for the first time volumetric 3D video made with Kartel.ai inside the AI world generated in gaussian splatting by World Labs !! I coded all in three.js! And it's possible to integrate elements on the fly ;) And so you can imagine soon what we will produce with that en AI : consistency of character and environment, relighting etc...
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More coming soon ! I manage to integrate for the first time volumetric 3D video made with Kartel.ai inside the AI world generated in gaussian splatting by World Labs !! I coded all in three.js! And it's possible to integrate elements on the fly ;) And so you can imagine soon what we will produce with that en AI : consistency of character and environment, relighting etc...

Lovis Odin

33,984 görüntüleme • 1 yıl önce

Getting a bunch of questions about this! It's a full gpu rendering pipeline with built in shader editor for Rive, which can interleave 2D + 3D. Think WebGPU (minus compute, for now) that runs anywhere, not just the browser. You can implement anything at all. Cell shading. Background blurs. Deformation effects. And yes, even Rive vector components as textures. Shaders are precompiled and ship with your .riv. Minimal runtime size impact. Still WIP.
0:13

Getting a bunch of questions about this! It's a full gpu rendering pipeline with built in shader editor for Rive, which can interleave 2D + 3D. Think WebGPU (minus compute, for now) that runs anywhere, not just the browser. You can implement anything at all. Cell shading. Background blurs. Deformation effects. And yes, even Rive vector components as textures. Shaders are precompiled and ship with your .riv. Minimal runtime size impact. Still WIP.

Guido Rosso

22,242 görüntüleme • 3 ay önce

Despite the Framer hype, we still get tons of requests for custom Next.js + Tailwind sites. Because most founders and their teams don't want to learn another tool. They want to own their code, not rent it. Just shipped a landing page in Next.js for a SaaS. Their reasoning: - Full control over performance optimization - No vendor lock-in - Developers can maintain it without learning Framer - Custom functionality without workarounds - Better SEO control Sure, Framer is faster to build. But he no-code movement sold everyone on "anyone can build websites now" and forgot that someone still needs to maintain them. What's your take: Custom code for control or no-code for speed?
0:13

Despite the Framer hype, we still get tons of requests for custom Next.js + Tailwind sites. Because most founders and their teams don't want to learn another tool. They want to own their code, not rent it. Just shipped a landing page in Next.js for a SaaS. Their reasoning: - Full control over performance optimization - No vendor lock-in - Developers can maintain it without learning Framer - Custom functionality without workarounds - Better SEO control Sure, Framer is faster to build. But he no-code movement sold everyone on "anyone can build websites now" and forgot that someone still needs to maintain them. What's your take: Custom code for control or no-code for speed?

Namya @ Supafast

50,594 görüntüleme • 9 ay önce

Gaussian splats don't simulate light. They capture it. I took one photograph of this room and generated it as a fully walkable 3D world in Mint. The volumetric rays, the shadow play on the rug, all preserved. This is what real-time 3D is becoming
0:13

Gaussian splats don't simulate light. They capture it. I took one photograph of this room and generated it as a fully walkable 3D world in Mint. The volumetric rays, the shadow play on the rug, all preserved. This is what real-time 3D is becoming

mint

15,446 görüntüleme • 1 ay önce

The West is not dying. It is being killed, and the names of the traitors are known. They occupy our capitals, infest our courts, pollute our newsrooms, and preach in our churches. They open the gates, kneel before the foreigner, and smirk as their own blood is driven from the land. They mock the fallen, defile the heroic, and spit on the blood that raised every city worth defending. They are not misguided. They are not mistaken. They are the enemy. They must be treated as such. For too long, we have been ruled by cowards, “men without chests,” by merchants loyal to nothing but the dollar, by liars who speak of progress while presiding over decay. A new generation now rises, armed not with apologies but with the fire of remembrance, with the memory of what we once were and the will to become greater still. We do not ask permission. We do not seek approval. We will reclaim what is ours, because no one else will. Victory will not come through debate. It will come through discipline, through will, through the unbreakable decision to endure, to outlast, and to return to the excellence and greatness that befit our people. We do not need millions. We require only a vanguard: men of loyalty, endurance, and resolve, hardened by truth and unmoved by fear. I say this not for approval, nor is it offered in hope of a reply, but in the spirit of doing what must be done. It is a promise made in full knowledge of what must come. The time of submission draws to a close. The age of reconquest begins. Let the traitors tremble. Let the weak, the feckless, and the unworthy fall away. The future belongs to those with the strength and the daring to seize it.
0:10

The West is not dying. It is being killed, and the names of the traitors are known. They occupy our capitals, infest our courts, pollute our newsrooms, and preach in our churches. They open the gates, kneel before the foreigner, and smirk as their own blood is driven from the land. They mock the fallen, defile the heroic, and spit on the blood that raised every city worth defending. They are not misguided. They are not mistaken. They are the enemy. They must be treated as such. For too long, we have been ruled by cowards, “men without chests,” by merchants loyal to nothing but the dollar, by liars who speak of progress while presiding over decay. A new generation now rises, armed not with apologies but with the fire of remembrance, with the memory of what we once were and the will to become greater still. We do not ask permission. We do not seek approval. We will reclaim what is ours, because no one else will. Victory will not come through debate. It will come through discipline, through will, through the unbreakable decision to endure, to outlast, and to return to the excellence and greatness that befit our people. We do not need millions. We require only a vanguard: men of loyalty, endurance, and resolve, hardened by truth and unmoved by fear. I say this not for approval, nor is it offered in hope of a reply, but in the spirit of doing what must be done. It is a promise made in full knowledge of what must come. The time of submission draws to a close. The age of reconquest begins. Let the traitors tremble. Let the weak, the feckless, and the unworthy fall away. The future belongs to those with the strength and the daring to seize it.

Chad Crowley

19,404 görüntüleme • 11 ay önce

We built the fastest 3D world generator capable of creating ANY enviornment Just dropped a few pictures of the Palace of Versailles It spat out a fully navigable 3D gaussian splat in seconds Not to mention its free to start
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We built the fastest 3D world generator capable of creating ANY enviornment Just dropped a few pictures of the Palace of Versailles It spat out a fully navigable 3D gaussian splat in seconds Not to mention its free to start

mint

13,342 görüntüleme • 2 ay önce

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.
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3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

AK

633,532 görüntüleme • 2 yıl önce

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.
0:16

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Aksel

1,264,068 görüntüleme • 2 ay önce

HE BUILT A $10,000-TIER ANIMATED SITE WITH CLAUDE CODE - FOR THE COST OF A SUBSCRIPTION What's on screen isn't a landing page with a parallax background It's a fully interactive, scroll-driven site with real-time 3D rendered in the browser What's actually on the page: > 3D product models rotating and reacting to scroll in real time via WebGL > Smooth hover interactions and transitions - no hand-coded keyframes > Cinematic minimal aesthetic that got featured on Awwwards > Typography layering, editorial layouts, everything assembled in one session What it normally takes: > A 3D artist, a motion designer, and a frontend developer > Weeks of handoffs - modelling, exporting, wiring animations, layout, copy > Six separate systems integrated by hand That pipeline was the moat. It's what justified the invoice The price gap: > Studio build at this level: $5,000-10,000+ > Your cost: a Claude subscription Timeline: weeks of production -> a single session Full walkthrough in the article below
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HE BUILT A $10,000-TIER ANIMATED SITE WITH CLAUDE CODE - FOR THE COST OF A SUBSCRIPTION What's on screen isn't a landing page with a parallax background It's a fully interactive, scroll-driven site with real-time 3D rendered in the browser What's actually on the page: > 3D product models rotating and reacting to scroll in real time via WebGL > Smooth hover interactions and transitions - no hand-coded keyframes > Cinematic minimal aesthetic that got featured on Awwwards > Typography layering, editorial layouts, everything assembled in one session What it normally takes: > A 3D artist, a motion designer, and a frontend developer > Weeks of handoffs - modelling, exporting, wiring animations, layout, copy > Six separate systems integrated by hand That pipeline was the moat. It's what justified the invoice The price gap: > Studio build at this level: $5,000-10,000+ > Your cost: a Claude subscription Timeline: weeks of production -> a single session Full walkthrough in the article below

monokern

696,859 görüntüleme • 15 gün önce