13 citations · 17 across the 4 of their papers we have counts for
6 papers · 1 filter
ERF: Explicit Radiance Field Reconstruction From Scratch
Samir Aroudj, Steven Lovegrove, Eddy Ilg +3
We propose a novel explicit dense 3D reconstruction approach that processes a set of images of a scene with sensor poses and calibrations and estimates a photo-real digital model.…
Identity-Disentangled Neural Deformation Model for Dynamic Meshes
Binbin Xu, Lingni Ma, Yuting Ye +3
Neural shape models can represent complex 3D shapes with a compact latent space. When applied to dynamically deforming shapes such as the human hands, however, they would need to p…
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
Wentao Yuan, Zhaoyang Lv, Tanner Schmidt +1
We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotati…
FroDO: From Detections to 3D Objects
Kejie Li, Martin Rünz, Meng Tang +8
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg +4
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a…
Dynamic High Resolution Deformable Articulated Tracking
Aaron Walsman, Weilin Wan, Tanner Schmidt +1
The last several years have seen significant progress in using depth cameras for tracking articulated objects such as human bodies, hands, and robotic manipulators. Most approaches…