190 citations · 998 across the 71 of their papers we have counts for
11 papers · 1 filter
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior
Davis Rempe, Jonah Philion, Leonidas J. Guibas +2
Evaluating and improving planning for autonomous vehicles requires scalable generation of long-tail traffic scenarios. To be useful, these scenarios must be realistic and challengi…
Mix3D: Out-of-Context Data Augmentation for 3D Scenes
Alexey Nekrasov, Jonas Schult, Or Litany +2
We present Mix3D, a data augmentation technique for segmenting large-scale 3D scenes. Since scene context helps reasoning about object semantics, current works focus on models with…
Neural Fields as Learnable Kernels for 3D Reconstruction
Francis Williams, Zan Gojcic, Sameh Khamis +4
We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art results wh…
Neural Fields in Visual Computing and Beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito +7
Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physica…
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Wenzheng Chen, Joey Litalien, Jun Gao +5
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers. Many previous learning-based approaches fo…
StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects
Ge Zhang, Or Litany, Srinath Sridhar +1
We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images. Reconstructing general articulating object categori…
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Congyue Deng, Or Litany, Yueqi Duan +3
Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds. Yet most proposed methods either use complex mathema…
Learning Spectral Unions of Partial Deformable 3D Shapes
Luca Moschella, Simone Melzi, Luca Cosmo +5
Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isome…
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson +2
The success of learning with noisy labels (LNL) methods relies heavily on the success of a warm-up stage where standard supervised training is performed using the full (noisy) trai…
Weakly Supervised Learning of Rigid 3D Scene Flow
Zan Gojcic, Or Litany, Andreas Wieser +2
We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies. At the co…
Self-Supervised Equivariant Scene Synthesis from Video
Cinjon Resnick, Or Litany, Cosmas Heiß +3
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations. Our method cap…