Search Results for author:
Found 15 papers, 12 papers with code
SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications.
Reconstructing Animatable Categories from Videos
Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories.
3D Shape Reconstruction from Videos
Dynamic Reconstruction
+1
Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis
Given a minute-long RGBD video of people interacting with their pets, we render the scene from novel camera trajectories derived from the in-scene motion of actors: (1) egocentric cameras that simulate the point of view of a target actor and (2) 3rd-person cameras that follow the actor.
3D-aware Conditional Image Synthesis
We propose pix2pix3D, a 3D-aware conditional generative model for controllable photorealistic image synthesis.
Distilling Neural Fields for Real-Time Articulated Shape Reconstruction
We present a method for reconstructing articulated 3D models from videos in real-time, without test-time optimization or manual 3D supervision at training time.
PPR: Physically Plausible Reconstruction from Monocular Videos
Given monocular videos, we build 3D models of articulated objects and environments whose 3D configurations satisfy dynamics and contact constraints.
BANMo: Building Animatable 3D Neural Models from Many Casual Videos
Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization, and (3) canonical embeddings that generate correspondences between pixels and an articulated model.
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
The surface embeddings are implemented as coordinate-based MLPs that are fit to each video via consistency and contrastive reconstruction losses. Experimental results show that ViSER compares favorably against prior work on challenging videos of humans with loose clothing and unusual poses as well as animals videos from DAVIS and YTVOS.
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
NeRS learns a neural shape representation of a closed surface that is diffeomorphic to a sphere, guaranteeing water-tight reconstructions.
LASR: Learning Articulated Shape Reconstruction from a Monocular Video
Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images.
Learning to Segment Rigid Motions from Two Frames
Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations.
Upgrading Optical Flow to 3D Scene Flow Through Optical Expansion
We describe an approach for upgrading 2D optical flow to 3D scene flow.
Hierarchical Deep Stereo Matching on High-resolution Images
We explore the problem of real-time stereo matching on high-res imagery.
Inferring Distributions Over Depth from a Single Image
Such approaches cannot diagnose when failures might occur.
Volumetric Correspondence Networks for Optical Flow
As a result, SOTA networks also employ various heuristics designed to limit volumetric processing, leading to limited accuracy and overfitting.
Ranked #14 on
Optical Flow Estimation
on KITTI 2015 (train)
