526 citations · 1.4k across the 51 of their papers we have counts for
9 papers · 1 filter
Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences
Norman Müller, Yu-Shiang Wong, Niloy J. Mitra +2
Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having th…
Towards Part-Based Understanding of RGB-D Scans
Alexey Bokhovkin, Vladislav Ishimtsev, Emil Bogomolov +4
Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-gra…
Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction
Aljaž Božič, Pablo Palafox, Michael Zollhöfer +3
We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation grap…
Forecasting Characteristic 3D Poses of Human Actions
Christian Diller, Thomas Funkhouser, Angela Dai
We propose the task of forecasting characteristic 3d poses: from a short sequence observation of a person, predict a future 3d pose of that person in a likely action-defining, char…
Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin +1
Object recognition has seen significant progress in the image domain, with focus primarily on 2D perception. We propose to leverage existing large-scale datasets of 3D models to un…
SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans
Angela Dai, Yawar Siddiqui, Justus Thies +2
We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a s…
Neural Non-Rigid Tracking
Aljaž Božič, Pablo Palafox, Michael Zollhöfer +3
We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables state-of-the-art non-rigid reconstruction by a learned robust optimization. Given two inpu…
SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans
Armen Avetisyan, Tatiana Khanova, Christopher Choy +3
We present a novel approach to reconstructing lightweight, CAD-based representations of scanned 3D environments from commodity RGB-D sensors. Our key idea is to jointly optimize fo…
Adversarial Texture Optimization from RGB-D Scans
Jingwei Huang, Justus Thies, Angela Dai +5
Realistic color texture generation is an important step in RGB-D surface reconstruction, but remains challenging in practice due to inaccuracies in reconstructed geometry, misalign…