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Introducing sigmaPBR - our new PBR solver which is currently in closed beta testing. sigmaPBR uses photometric images, camera poses, and input geometry to reconstruct accurate physically based material properties for use in rendering, ML training and 3D scanning pipelines.

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McGavish

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26,044 görüntüleme • 10 gün önce •via X (Twitter)

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New scan sample, old wooden container scanned using our advanced robotic scanner developed at INVOLV3D and inciprocal software for PBR material processing. You can literally smell this wood! #3dscanning #pbr #b3d
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New scan sample, old wooden container scanned using our advanced robotic scanner developed at INVOLV3D and inciprocal software for PBR material processing. You can literally smell this wood! #3dscanning #pbr #b3d

McGavish

12,902 görüntüleme • 2 yıl önce

📢 Automatic PBR : Image-to-material (I2M) 👉Textured Mesh in > Full PBR Mesh out Our latest foundation model for enriching existing 3D assets with the full array of PBR materials, whilst maintaining the same mesh geometry and UV unwrap. #I2M #PBR #CV #Marso #texturing #gamedev #computervision M-XR
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📢 Automatic PBR : Image-to-material (I2M) 👉Textured Mesh in > Full PBR Mesh out Our latest foundation model for enriching existing 3D assets with the full array of PBR materials, whilst maintaining the same mesh geometry and UV unwrap. #I2M #PBR #CV #Marso #texturing #gamedev #computervision M-XR

Elliott Round

13,423 görüntüleme • 5 ay önce

That's a hammer time again - just another prop from recent random scans - full PBR stack, automated capture, zero manual cleanup - just plug & play in your engine. We provide top quality 3D scanning services for games, film, fashion, and e-commerce.
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That's a hammer time again - just another prop from recent random scans - full PBR stack, automated capture, zero manual cleanup - just plug & play in your engine. We provide top quality 3D scanning services for games, film, fashion, and e-commerce.

McGavish

19,213 görüntüleme • 1 yıl önce

We can 3D scan even the hardest objects - dark, low-contrast, and nearly featureless surfaces are no longer a challenge - all with full PBR material stack. Need this level of precision for your production? Let me know! #photogrammetry #photometry
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We can 3D scan even the hardest objects - dark, low-contrast, and nearly featureless surfaces are no longer a challenge - all with full PBR material stack. Need this level of precision for your production? Let me know! #photogrammetry #photometry

McGavish

45,178 görüntüleme • 1 yıl önce

Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.
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Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.

AK

96,527 görüntüleme • 2 yıl önce

We're currently testing new set design and camera placement AI processes for our portfolio! We're using colored markers to place our "cameras" and characters in specific locations on a specific set! it's similar to preparing a set and scenery for a real film. AI Tools : Midjourney (Set Place 2D) /Freepik (now Magnific) : Nano Banana Pro (3D and Camera / character placement) / Grok + Kling (Animation) / Topaz (Upscale) / Characters (See process in quote)
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We're currently testing new set design and camera placement AI processes for our portfolio! We're using colored markers to place our "cameras" and characters in specific locations on a specific set! it's similar to preparing a set and scenery for a real film. AI Tools : Midjourney (Set Place 2D) /Freepik (now Magnific) : Nano Banana Pro (3D and Camera / character placement) / Grok + Kling (Animation) / Topaz (Upscale) / Characters (See process in quote)

Anima

139,457 görüntüleme • 7 ay önce

Scanning large surface with PBR maps? No problem! Still polishing the methodologies and approach but initial tests are very promising. Here is a 130 x 50 cm tabletop, tricky reflective surface especially if we are aiming for smooth reconstruction.
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Scanning large surface with PBR maps? No problem! Still polishing the methodologies and approach but initial tests are very promising. Here is a 130 x 50 cm tabletop, tricky reflective surface especially if we are aiming for smooth reconstruction.

McGavish

18,326 görüntüleme • 1 yıl önce

Who needs a mocap suit? 😁Testing out AIOS - All-in-One-Stage Expressive Human Pose and Shape Estimation (link in thread). I uploaded a video from Pexels and it created the 3D mesh, which I was able to import into Blender and use Geometry nodes to control. (see thread for nodes).
0:15

Who needs a mocap suit? 😁Testing out AIOS - All-in-One-Stage Expressive Human Pose and Shape Estimation (link in thread). I uploaded a video from Pexels and it created the 3D mesh, which I was able to import into Blender and use Geometry nodes to control. (see thread for nodes).

👣KᗪᗩᗯG👣 🔶

11,560 görüntüleme • 1 yıl önce

Super excited to announce our new work: Synthesizing Moving People with 3D Control (3DHM)💡 Why is 3DHM unique? With 3D Control, 3DHM can animate a 𝗿𝗮𝗻𝗱𝗼𝗺 human photo with 𝗮𝗻𝘆 poses in a 𝟯𝟲𝟬-𝗱𝗲𝗴𝗿𝗲𝗲 camera view and 𝗮𝗻𝘆 camera azimuths from 𝗮𝗻𝘆 video!
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Super excited to announce our new work: Synthesizing Moving People with 3D Control (3DHM)💡 Why is 3DHM unique? With 3D Control, 3DHM can animate a 𝗿𝗮𝗻𝗱𝗼𝗺 human photo with 𝗮𝗻𝘆 poses in a 𝟯𝟲𝟬-𝗱𝗲𝗴𝗿𝗲𝗲 camera view and 𝗮𝗻𝘆 camera azimuths from 𝗮𝗻𝘆 video!

Boyi Li

74,482 görüntüleme • 2 yıl önce

Tripo P1.0 is live. Introducing Smart Mesh in Tripo Studio — generate structured 3D meshes in ~2 seconds. ⚡ ~2s generation 🧠 Smart topology 🛠 Production-ready assets 🌍 Built for game pipelines, real-time rendering, web 3D tools, and scalable content production From prompt to usable mesh — almost instantly. Try it: #3DAI #3DCG #3dart #Tripo
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Tripo P1.0 is live. Introducing Smart Mesh in Tripo Studio — generate structured 3D meshes in ~2 seconds. ⚡ ~2s generation 🧠 Smart topology 🛠 Production-ready assets 🌍 Built for game pipelines, real-time rendering, web 3D tools, and scalable content production From prompt to usable mesh — almost instantly. Try it: #3DAI #3DCG #3dart #Tripo

Tripo

44,954 görüntüleme • 6 ay önce

DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery Abstract: Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.
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DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery Abstract: Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.

MrNeRF

21,389 görüntüleme • 1 yıl önce

We are making the power of real-time 3D more accessible 🌐 We’re getting ready to officially launch Unity Studio, our web-based editor designed for industries like manufacturing, automotive, healthcare, architecture, and more to build, interact and iterate in 3D easily. In the meantime, our Open Beta is active right now. Get in early, explore the interface, and start building before the official GA rollout. Start building in your browser: #UnityStudio #UnityIndustry
0:10

We are making the power of real-time 3D more accessible 🌐 We’re getting ready to officially launch Unity Studio, our web-based editor designed for industries like manufacturing, automotive, healthcare, architecture, and more to build, interact and iterate in 3D easily. In the meantime, our Open Beta is active right now. Get in early, explore the interface, and start building before the official GA rollout. Start building in your browser: #UnityStudio #UnityIndustry

Unity

66,504 görüntüleme • 6 ay önce

hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design
0:29

hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design

Matthieu LC

45,587 görüntüleme • 1 yıl önce