Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

PE3R Perception-Efficient 3D Reconstruction

_akhaliq's profile picture

AK

449,949 subscribers

28,372 Aufrufe • vor 1 Jahr •via X (Twitter)

BildungWissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

RGB Human Videos -> Dexterous Manipulation Policies No teleoperation. No wearables. No robot interaction. The 🔑: Reliable 3D hand-object trajectory reconstruction and efficient action augmentation Fun collaboration led by Hongyi Chen
0:15

RGB Human Videos -> Dexterous Manipulation Policies No teleoperation. No wearables. No robot interaction. The 🔑: Reliable 3D hand-object trajectory reconstruction and efficient action augmentation Fun collaboration led by Hongyi Chen

Homanga Bharadhwaj @ CVPR

10,761 Aufrufe • vor 3 Monaten

Liver Segments: Axial CT and 3D Reconstruction Source: xray_iq
0:17

Liver Segments: Axial CT and 3D Reconstruction Source: xray_iq

Yakup Ergün

35,428 Aufrufe • vor 10 Monaten

Human Hair Reconstruction with Strand-Aligned 3D Gaussians Contributions (cited): – We propose a new 3D line lifting scheme that uses a modified 3DGS reconstruction technique to lift 2D orientation maps into a 3D field while also providing refinement of the camera parameters; – We introduce a dual representation of hair strand polylines and 3D Gaussians to achieve differentiable rasterization of hair strands and leverage photometric constraints for strand-based hair reconstruction; – Based on these components, we propose a coarse-to-fine optimization method for prior-guided hair reconstruction that leverages both latent and explicit representations of the hairstyle.
0:13

Human Hair Reconstruction with Strand-Aligned 3D Gaussians Contributions (cited): – We propose a new 3D line lifting scheme that uses a modified 3DGS reconstruction technique to lift 2D orientation maps into a 3D field while also providing refinement of the camera parameters; – We introduce a dual representation of hair strand polylines and 3D Gaussians to achieve differentiable rasterization of hair strands and leverage photometric constraints for strand-based hair reconstruction; – Based on these components, we propose a coarse-to-fine optimization method for prior-guided hair reconstruction that leverages both latent and explicit representations of the hairstyle.

MrNeRF

106,479 Aufrufe • vor 1 Jahr

"Spann3R: 3D Reconstruction with Spatial Memory" In a nutshell: DUSt3R strikes again! Paper: Project: Method ⬇️
0:14

"Spann3R: 3D Reconstruction with Spatial Memory" In a nutshell: DUSt3R strikes again! Paper: Project: Method ⬇️

MrNeRF

52,560 Aufrufe • vor 1 Jahr

image -> 3D scene -> camera path -> wan1.3 v2v Pretty insane results. Playing with reconstruction now ...
0:10

image -> 3D scene -> camera path -> wan1.3 v2v Pretty insane results. Playing with reconstruction now ...

martin_casado

28,525 Aufrufe • vor 10 Monaten

Learn how to combine a deep learning model for pose estimation to perform a 3D reconstruction using two cameras 📸
0:13

Learn how to combine a deep learning model for pose estimation to perform a 3D reconstruction using two cameras 📸

MATLAB

15,665 Aufrufe • vor 1 Jahr

Lightplane Highly-Scalable Components for Neural 3D Fields Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a
0:12

Lightplane Highly-Scalable Components for Neural 3D Fields Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a

AK

18,896 Aufrufe • vor 2 Jahren

Super efficient
0:14

Super efficient

HOW THINGS WORK

102,088 Aufrufe • vor 10 Monaten

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.
0:12

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,801 Aufrufe • vor 1 Jahr

Human3R basically says "what if we stopped treating human reconstruction & scene reconstruction as separate problems." Feed it a casual 2D videos and it spits out multiple people's full body models + 3D scene + camera trajectory - all at once, no multi-stage pipeline required.
0:12

Human3R basically says "what if we stopped treating human reconstruction & scene reconstruction as separate problems." Feed it a casual 2D videos and it spits out multiple people's full body models + 3D scene + camera trajectory - all at once, no multi-stage pipeline required.

Bilawal Sidhu

21,632 Aufrufe • vor 8 Monaten

Announcing 2 new members of the *St3R family for human-centric 3D vision tasks! Meet HAMst3R & HOSt3R #ICCV2025 #iccv2025 - HAMSt3R (Human-Aware Multi-view Stereo 3D Reconstruction) extends MASt3R to handle scenes involving people. Paper: 1/2 🧵
0:10

Announcing 2 new members of the *St3R family for human-centric 3D vision tasks! Meet HAMst3R & HOSt3R #ICCV2025 #iccv2025 - HAMSt3R (Human-Aware Multi-view Stereo 3D Reconstruction) extends MASt3R to handle scenes involving people. Paper: 1/2 🧵

NAVER LABS Europe

14,051 Aufrufe • vor 9 Monaten

Nuvo: Neural UV Mapping! It's super difficult to UV map/texture atlas geometry produced by 3D reconstruction and generation pipelines. Nuvo works on all kinds of "unruly" 3D representations (NeRF, DreamFusion, etc.) and enables easy appearance editing! 1/3
0:12

Nuvo: Neural UV Mapping! It's super difficult to UV map/texture atlas geometry produced by 3D reconstruction and generation pipelines. Nuvo works on all kinds of "unruly" 3D representations (NeRF, DreamFusion, etc.) and enables easy appearance editing! 1/3

Pratul Srinivasan

20,842 Aufrufe • vor 2 Jahren

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.
0:19

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

21,340 Aufrufe • vor 1 Jahr

Oh wow! I just tested Splatt3R with my own data on my computer, which creates 3D Gaussian Splats at 4 FPS on uncalibrated 512x512 2D images! It's by far the fastest 3D reconstruction method, powered by MASt3R. Check out the video!
0:17

Oh wow! I just tested Splatt3R with my own data on my computer, which creates 3D Gaussian Splats at 4 FPS on uncalibrated 512x512 2D images! It's by far the fastest 3D reconstruction method, powered by MASt3R. Check out the video!

MrNeRF

67,464 Aufrufe • vor 1 Jahr

If you’re not in freefall, you’re likely contacting something. Yet 3D human-object interaction (HOI) reconstruction remains underexplored. PICO (#CVPR2025) recovers humans 🏃‍♂️, objects 🏓, and their interactions 👉🍎- all in 3D, from just a single internet image. 1/11
0:12

If you’re not in freefall, you’re likely contacting something. Yet 3D human-object interaction (HOI) reconstruction remains underexplored. PICO (#CVPR2025) recovers humans 🏃‍♂️, objects 🏓, and their interactions 👉🍎- all in 3D, from just a single internet image. 1/11

Shashank Tripathi

24,064 Aufrufe • vor 1 Jahr

Here's an amazing 3D Reconstruction from a Coronary CTA of a 25-year-old woman who suddenly collapsed to the ground in cardiac arrest while running What's the diagnosis?
0:14

Here's an amazing 3D Reconstruction from a Coronary CTA of a 25-year-old woman who suddenly collapsed to the ground in cardiac arrest while running What's the diagnosis?

Sam Ghali, M.D.

318,715 Aufrufe • vor 8 Monaten

CVPR Highlight #1 VGGT: Visual Geometry Grounded Transformer (best paper award) 🚀 Super-fast 3D scene reconstruction 🤗 Model and demo on Hugging Face The result is a GLB point cloud 👇
0:15

CVPR Highlight #1 VGGT: Visual Geometry Grounded Transformer (best paper award) 🚀 Super-fast 3D scene reconstruction 🤗 Model and demo on Hugging Face The result is a GLB point cloud 👇

dylan

19,325 Aufrufe • vor 11 Monaten