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What If Your ESP32 Could Run Real 3D Graphics Without a GPU? 🤯 Jet is a tiny C++17 software 3D rasteriser built for constrained hardware like the ESP32 and STM32. - Runs entirely in software - Integer-only rendering - Native RGB565 - Flat, Gouraud and Phong shading - Affine...

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Praveen Kumar Verma

10,552 subscribers

54,709 просмотров • 1 месяц назад •via X (Twitter)

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Комментарии: 38

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

That's seriously impressive for a microcontroller doing the rendering without a GPU. Credits: @CubeCoders

Фото профиля pikuma.com
pikuma.com1 месяц назад

This looks beautiful. I'll check the code later! ❤️ Is the ESP32-S3 running at 240 MHz? It's still impressive either way.

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Indeed it is.

Фото профиля ovoDRIZZYxo
ovoDRIZZYxo1 месяц назад

U guys never heard about commodore? ;)

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

No, I did not hear this before.

Фото профиля aap
aap1 месяц назад

Oh, this is very cool. i built a tiny 3d renderer for the ESP32 as well some time ago. yours looks much more serious though! maybe i should see how far i can push mine... the code:

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

This is good actually

Фото профиля SuperBenji63 - TERFs hate account
SuperBenji63 - TERFs hate account1 месяц назад

Seems we are building the Ps1 again 😁💪 That really impressive, there is no limit for this little chip

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

True.

Фото профиля Catnip
Catnip1 месяц назад

Very cool, not sure how applicable it is though, most rendering at 480*320 pixels is fairly quick. Neat though 60fps is great

Фото профиля Pippinware
Pippinware1 месяц назад

Dude… 42 years ago, Frontier Elite 2 had 3D graphics on MSDOS… This is a pretty low bar you are clearing.

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Frontier elite 2 was released in 1993, so it is 33 years ago not 42

Фото профиля Pippinware
Pippinware1 месяц назад

Yes

Фото профиля DOCTOR DEMO
DOCTOR DEMO1 месяц назад

Impressive, very nice, now let's see Sonic Adventure 2 running on it

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Yeah

Фото профиля MK
MK1 месяц назад

Nice and cool, for old timers and some classy effects

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

We can also modify them

Фото профиля TechTrendDude
TechTrendDude1 месяц назад

Very cool 👍

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Code does awesome work.

Фото профиля Christian
Christian1 месяц назад

Cable management on point

Фото профиля strips
strips1 месяц назад

>literally better than the PS1

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Yes, it definitely seems better.

Фото профиля Wimukthi
Wimukthi1 месяц назад

We were doing this on much slower CPU's decades ago.

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Agree

Фото профиля Austin
Austin1 месяц назад

Wow impressive numbers

Фото профиля Dan
Dan1 месяц назад

Very cool and impressive for esp32

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

It is amazing to feel this.

Фото профиля 🩶🤍🖤🤎❤️🧡💛💚💙💜
🩶🤍🖤🤎❤️🧡💛💚💙💜1 месяц назад

I can hardly get the SPI screen to run a stable 60.

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Try now.

Фото профиля slopcel
slopcel1 месяц назад

does it run crysis?

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Did not try it.

Фото профиля NoName
NoName1 месяц назад

Ignore those westren bigot white men comments ... just do what u feel enjoy able fulfilling

Фото профиля 🏴‍☠️ 🅢🅐🅘🅝🅣🅗🅔🅛🅛 🏴‍☠️
🏴‍☠️ 🅢🅐🅘🅝🅣🅗🅔🅛🅛 🏴‍☠️1 месяц назад

does it work with rp2350?

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

It will work but performance depends on drivers and modules it is using.

Фото профиля 🏴‍☠️ 🅢🅐🅘🅝🅣🅗🅔🅛🅛 🏴‍☠️
🏴‍☠️ 🅢🅐🅘🅝🅣🅗🅔🅛🅛 🏴‍☠️1 месяц назад

Interesting, thanks.

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Good to know. Thanks

Фото профиля JPCR-96
JPCR-961 месяц назад

Can it run Quake?

Фото профиля Praveen Kumar Verma
Praveen Kumar Verma1 месяц назад

Yes it is possible with little tweaks.

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Guri Singh

2,967,409 просмотров • 6 месяцев назад

Here is the Geometry Nodes Weighted Normals with Laplacian Blur on a full character (a vroid). It easily improves the shading even on game topology with almost no setup. I built this as part of my quest to improve real time toon shading. 3D anime models are popular, but use of dynamic light is rare even among high quality vtuber models. This is for several reasons, but a big one is simply that it takes a lot of Custom Normals work to make 3D cel shading not look like a jagged mess (other pieces of the puzzle are issues like deformations, multiple lights, etc). And fixing Normals is tedious, especially on existing game topology. I have focused on proxy meshes for priority areas like character faces, but they aren't an efficient solution for the whole body + outfit. I wanted something I could just throw on any model and make it at least not a jagged mess anymore even if it wasn't perfect. As you can see from this clip, this does that very well! And vertex groups can be used to control the style of the effect and power. It still can't smooth beyond what the topology density can support, but the topo itself is no longer a problem (for higher res, could be run on a subdivided version of the model and then baked to a Normal Map.) The only changes I made to this model were adding a weld modifier to merge split edges during interpolation, and a vertex group to select the skirt. I have not yet added full handling and logic for detecting edges with big angles like the skirt, or for handling boundaries like on the hair, so both those areas can get better too. You can also see that while it successfully smooths out the Face, it isn't really stylistically correct there. That is still best done with a proxy mesh to define a new shape. This is part of the tools I am working on for Fondant. We are putting together a Blender Addon to release this + a proxy mesh tool for the face, and are working on resolving other problems in-engine to fully bring dynamic light to real time 3D toon shading. Give us a follow, and send them a DM if you are interested in testing these tools as they develop!
2:17

Here is the Geometry Nodes Weighted Normals with Laplacian Blur on a full character (a vroid). It easily improves the shading even on game topology with almost no setup. I built this as part of my quest to improve real time toon shading. 3D anime models are popular, but use of dynamic light is rare even among high quality vtuber models. This is for several reasons, but a big one is simply that it takes a lot of Custom Normals work to make 3D cel shading not look like a jagged mess (other pieces of the puzzle are issues like deformations, multiple lights, etc). And fixing Normals is tedious, especially on existing game topology. I have focused on proxy meshes for priority areas like character faces, but they aren't an efficient solution for the whole body + outfit. I wanted something I could just throw on any model and make it at least not a jagged mess anymore even if it wasn't perfect. As you can see from this clip, this does that very well! And vertex groups can be used to control the style of the effect and power. It still can't smooth beyond what the topology density can support, but the topo itself is no longer a problem (for higher res, could be run on a subdivided version of the model and then baked to a Normal Map.) The only changes I made to this model were adding a weld modifier to merge split edges during interpolation, and a vertex group to select the skirt. I have not yet added full handling and logic for detecting edges with big angles like the skirt, or for handling boundaries like on the hair, so both those areas can get better too. You can also see that while it successfully smooths out the Face, it isn't really stylistically correct there. That is still best done with a proxy mesh to define a new shape. This is part of the tools I am working on for Fondant. We are putting together a Blender Addon to release this + a proxy mesh tool for the face, and are working on resolving other problems in-engine to fully bring dynamic light to real time 3D toon shading. Give us a follow, and send them a DM if you are interested in testing these tools as they develop!

aVersionOfReality

14,904 просмотров • 1 год назад

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.
4:44

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

Melvin

34,306 просмотров • 3 месяцев назад

A guy named Philip turned every old iPhone and iPad into a working second monitor for free. It is called OpenDisplay. It gives your Mac a real extended display using an Apple device you already own. You can use it for: > Messages > Spotify > Documentation > Video previews > Terminal windows > Reference material Download the sender on your Mac and the receiver on your iPhone or iPad. Connect them using WiFi or the charging cable. Your Mac creates another display, and you can drag any window onto it exactly like a normal monitor. The screen runs at native Retina resolution and can reach 60 FPS over USB. You can tap to click, drag windows with your finger, scroll using two fingers, and rotate the device into portrait mode. It even supports several devices at the same time. What I really like about this is that it doesn't need an account or subscription or some special dongle. Your screen travels directly between your own devices. Philip built the whole project alone and released the source code on GitHub. Your old iPhone was never useless. It was just waiting for someone to ignore Apple’s rules.
0:47

A guy named Philip turned every old iPhone and iPad into a working second monitor for free. It is called OpenDisplay. It gives your Mac a real extended display using an Apple device you already own. You can use it for: > Messages > Spotify > Documentation > Video previews > Terminal windows > Reference material Download the sender on your Mac and the receiver on your iPhone or iPad. Connect them using WiFi or the charging cable. Your Mac creates another display, and you can drag any window onto it exactly like a normal monitor. The screen runs at native Retina resolution and can reach 60 FPS over USB. You can tap to click, drag windows with your finger, scroll using two fingers, and rotate the device into portrait mode. It even supports several devices at the same time. What I really like about this is that it doesn't need an account or subscription or some special dongle. Your screen travels directly between your own devices. Philip built the whole project alone and released the source code on GitHub. Your old iPhone was never useless. It was just waiting for someone to ignore Apple’s rules.

Ihtesham Ali

73,857 просмотров • 1 месяц назад

Holy shit... the gap between what AI video tools promise and what they actually hand back just got a lot smaller. Normally, using AI for video feels like a gamble: you type in a prompt, wait three minutes, and it spits back a locked MP4. If one single specs number on screen is wrong, you can't edit it—you just have to re-prompt, re-render, and burn through your credits again. To see if any tool could actually handle a real commercial asset without the usual headache, I ran a quick test. I took a concept photo of an "iPhone Duo" and asked Fotor’s Fotor Agent to build an Apple-style product reveal—no 3D modeling, no physical camera setup, and no hiring a motion designer. It didn't just render a clip. It handed me an actual multi-track project file in about 40 minutes. A few things genuinely surprised me about the workflow: 1. It built a real timeline (and did its own research) I uploaded the concept shot and asked for a reveal with animated specs. I didn't even feed it detailed technical specs—it actually went online, pulled plausible data, and mapped it out. I cross-checked against Apple’s actual site later and the formatting and spec logic were spot-on. Instead of a flat video, it laid out the clips, music, transitions, and motion graphics onto separate editable tracks. 2. Native 4K Motion Graphics for a few cents a second Getting clean, keyframed motion graphics usually means opening After Effects or paying someone a few thousand bucks to build them. Fotor Agent generated sharp, native 4K spec cards and charts right on the timeline. The cost broke down to just a few cents per second, which makes testing complex visual ideas almost risk-free. 3. You can fix mistakes without re-rendering everything This was the most practical part. When I wanted to shorten a piece of copy on one of the spec cards, I didn't have to generate a new video or burn more credits. I just clicked the text layer directly on the timeline, edited it, and the preview updated instantly. The actual breakdown: Time: 3–5 days of AE keyframing → ~40 minutes Cost: Thousands in agency quotes → A few dollars in credits Workflow: Jumping between 5 different apps → Done in one Agent AI shouldn't just dump a locked video file on your desktop and call it a day. Fotor Agent handles the heavy production setup while leaving you with an actual project file you can tweak and refine. Screen recording and the 4K test clip are attached below! 👇🏼
1:06

Holy shit... the gap between what AI video tools promise and what they actually hand back just got a lot smaller. Normally, using AI for video feels like a gamble: you type in a prompt, wait three minutes, and it spits back a locked MP4. If one single specs number on screen is wrong, you can't edit it—you just have to re-prompt, re-render, and burn through your credits again. To see if any tool could actually handle a real commercial asset without the usual headache, I ran a quick test. I took a concept photo of an "iPhone Duo" and asked Fotor’s Fotor Agent to build an Apple-style product reveal—no 3D modeling, no physical camera setup, and no hiring a motion designer. It didn't just render a clip. It handed me an actual multi-track project file in about 40 minutes. A few things genuinely surprised me about the workflow: 1. It built a real timeline (and did its own research) I uploaded the concept shot and asked for a reveal with animated specs. I didn't even feed it detailed technical specs—it actually went online, pulled plausible data, and mapped it out. I cross-checked against Apple’s actual site later and the formatting and spec logic were spot-on. Instead of a flat video, it laid out the clips, music, transitions, and motion graphics onto separate editable tracks. 2. Native 4K Motion Graphics for a few cents a second Getting clean, keyframed motion graphics usually means opening After Effects or paying someone a few thousand bucks to build them. Fotor Agent generated sharp, native 4K spec cards and charts right on the timeline. The cost broke down to just a few cents per second, which makes testing complex visual ideas almost risk-free. 3. You can fix mistakes without re-rendering everything This was the most practical part. When I wanted to shorten a piece of copy on one of the spec cards, I didn't have to generate a new video or burn more credits. I just clicked the text layer directly on the timeline, edited it, and the preview updated instantly. The actual breakdown: Time: 3–5 days of AE keyframing → ~40 minutes Cost: Thousands in agency quotes → A few dollars in credits Workflow: Jumping between 5 different apps → Done in one Agent AI shouldn't just dump a locked video file on your desktop and call it a day. Fotor Agent handles the heavy production setup while leaving you with an actual project file you can tweak and refine. Screen recording and the 4K test clip are attached below! 👇🏼

marcus

45,851 просмотров • 16 дней назад

I’m excited to share Deputies, an open source control plane for background coding agents! The goal is simple: move agentic software development off individual laptops and into a central hub where teams can see what agents are doing, review their outputs, and keep a traceable history of the work. Inspired by systems like Inspect/Open-Inspect, Minions, and Open SWE, I built Deputies on top of fks's Flue Framework. It’s designed to run in your own infrastructure and trust boundaries, with Slack/GitHub integrations and Daytona + Docker sandbox support.
8:51

I’m excited to share Deputies, an open source control plane for background coding agents! The goal is simple: move agentic software development off individual laptops and into a central hub where teams can see what agents are doing, review their outputs, and keep a traceable history of the work. Inspired by systems like Inspect/Open-Inspect, Minions, and Open SWE, I built Deputies on top of fks's Flue Framework. It’s designed to run in your own infrastructure and trust boundaries, with Slack/GitHub integrations and Daytona + Docker sandbox support.

Sid Palas

13,036 просмотров • 4 месяцев назад

Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!
0:29

Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!

Bilawal Sidhu

704,409 просмотров • 1 год назад

I built a Three.js rendering study inspired by Tiny Glade’s painterly aesthetic, and got it running at 120fps in the browser. Over the past few weeks, I’ve been studying how stylized games achieve that soft, handcrafted look in real time. Tiny Glade was a huge inspiration, and I wanted to use the browser as a constraint: no compute shaders, no native GPU access, and single-threaded JavaScript. As part of this study, I implemented: - GPU-driven instanced brick walls with procedural noise jitter and elastic build animations - Tree, bush, and flower rendering with billboard card expansion, wind sway, and grow animations - Procedural grass with terrain conformance and interactive push deformation - Animated water with layered noise, interactive ripples, and Fresnel-based reflections - Procedural terrain with slope-aware triplanar materials, dirt paths, and rocks - A 7-pass post-processing stack with TAA, bloom, depth of field, painterly filtering, ACES tonemapping, 3D LUT color grading, and film grain The hardest part wasn’t writing any single shader. It was making all of these systems work together at high frame rates inside WebGL, where every millisecond counts and performance problems compound quickly across animation, materials, post-processing, and scene management. Some techniques in this study were inspired by analyzing Tiny Glade’s rendering approach, while others were original implementations built from scratch from visual reference. That contrast taught me a lot: recreating an effect is one challenge, but designing your own shaders and systems to achieve a similar feel is a very different one. This is a private educational rendering study. Some temporary placeholder content is being used during the research phase, and any public or production version would use original or properly licensed assets. Huge credit to Pounce Light for the incredible art direction and rendering work in Tiny Glade: Three.js #gamedev #webgl #threejs #rendering #graphics #realtimerendering #shaderdev
7:21

I built a Three.js rendering study inspired by Tiny Glade’s painterly aesthetic, and got it running at 120fps in the browser. Over the past few weeks, I’ve been studying how stylized games achieve that soft, handcrafted look in real time. Tiny Glade was a huge inspiration, and I wanted to use the browser as a constraint: no compute shaders, no native GPU access, and single-threaded JavaScript. As part of this study, I implemented: - GPU-driven instanced brick walls with procedural noise jitter and elastic build animations - Tree, bush, and flower rendering with billboard card expansion, wind sway, and grow animations - Procedural grass with terrain conformance and interactive push deformation - Animated water with layered noise, interactive ripples, and Fresnel-based reflections - Procedural terrain with slope-aware triplanar materials, dirt paths, and rocks - A 7-pass post-processing stack with TAA, bloom, depth of field, painterly filtering, ACES tonemapping, 3D LUT color grading, and film grain The hardest part wasn’t writing any single shader. It was making all of these systems work together at high frame rates inside WebGL, where every millisecond counts and performance problems compound quickly across animation, materials, post-processing, and scene management. Some techniques in this study were inspired by analyzing Tiny Glade’s rendering approach, while others were original implementations built from scratch from visual reference. That contrast taught me a lot: recreating an effect is one challenge, but designing your own shaders and systems to achieve a similar feel is a very different one. This is a private educational rendering study. Some temporary placeholder content is being used during the research phase, and any public or production version would use original or properly licensed assets. Huge credit to Pounce Light for the incredible art direction and rendering work in Tiny Glade: Three.js #gamedev #webgl #threejs #rendering #graphics #realtimerendering #shaderdev

Ibrahim Boona

58,625 просмотров • 6 месяцев назад

3D scanning and rendering is moving so fast - got my splats up and running and I'm mind blown getting ~100fps for this complex 3D scene ⬇️ 🤯 1. WAY faster than NeRF: For comparison, NeRFs would takes around 10 seconds per frame (!) Instead I'm zipping around with FPV controls without breaking a sweat - though I do crash a few times towards the end of the video lol 2. Old Meets New: Gaussian Splatting is cool in that it fuses classical graphics and deep learning techniques. Like NeRFs, this is still a radiance field - just without the slower (ne)ural rendering part. 3. Explicit Representation: Instead you represent a 3D scene as a collection of ellipsoidal "splats" called gaussians. Each gaussian has a position, size, and color. Rendering in real-time is done by projecting into the image plane and alpha blending. 4. Photorealistic Effects: Gaussian splatting use spherical harmonics to represent the view-dependent effects and lighting - allowing surfaces to change color when viewed from different angles, enabling greater photorealism. It doesn't use a neural network, but the training loop is similar to deep learning. 5. Enables Direct Editing: But it's not just speed - with Gaussian Splatting you also get 3D editing support! So you can select, move, and delete stuff, even relight stuff. This type of editing has been more tedious to do with NeRFs and their implicit black box representations. 📲 More tests cooking! Much more to unpack here including simpler explanations. If you enjoyed this post, you might enjoy my feed: Bilawal Sidhu
0:35

3D scanning and rendering is moving so fast - got my splats up and running and I'm mind blown getting ~100fps for this complex 3D scene ⬇️ 🤯 1. WAY faster than NeRF: For comparison, NeRFs would takes around 10 seconds per frame (!) Instead I'm zipping around with FPV controls without breaking a sweat - though I do crash a few times towards the end of the video lol 2. Old Meets New: Gaussian Splatting is cool in that it fuses classical graphics and deep learning techniques. Like NeRFs, this is still a radiance field - just without the slower (ne)ural rendering part. 3. Explicit Representation: Instead you represent a 3D scene as a collection of ellipsoidal "splats" called gaussians. Each gaussian has a position, size, and color. Rendering in real-time is done by projecting into the image plane and alpha blending. 4. Photorealistic Effects: Gaussian splatting use spherical harmonics to represent the view-dependent effects and lighting - allowing surfaces to change color when viewed from different angles, enabling greater photorealism. It doesn't use a neural network, but the training loop is similar to deep learning. 5. Enables Direct Editing: But it's not just speed - with Gaussian Splatting you also get 3D editing support! So you can select, move, and delete stuff, even relight stuff. This type of editing has been more tedious to do with NeRFs and their implicit black box representations. 📲 More tests cooking! Much more to unpack here including simpler explanations. If you enjoyed this post, you might enjoy my feed: Bilawal Sidhu

Bilawal Sidhu

337,090 просмотров • 3 лет назад