Search Results for author: Christopher B. Choy

Found 8 papers, 5 papers with code

Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions

no code implementations NeurIPS 2021 Jiachen Sun, Yulong Cao, Christopher B. Choy, Zhiding Yu, Anima Anandkumar, Zhuoqing Morley Mao, Chaowei Xiao

In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds with adversarial training.

Adversarial Robustness Autonomous Driving +1

SEGCloud: Semantic Segmentation of 3D Point Clouds

no code implementations20 Oct 2017 Lyne P. Tchapmi, Christopher B. Choy, Iro Armeni, JunYoung Gwak, Silvio Savarese

Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation.

Weakly supervised 3D Reconstruction with Adversarial Constraint

2 code implementations31 May 2017 JunYoung Gwak, Christopher B. Choy, Animesh Garg, Manmohan Chandraker, Silvio Savarese

Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks.

3D Reconstruction

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

3 code implementations CVPR 2017 Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker

DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.

Future prediction Multi Future Trajectory Prediction +1

Scene Graph Generation by Iterative Message Passing

3 code implementations CVPR 2017 Danfei Xu, Yuke Zhu, Christopher B. Choy, Li Fei-Fei

In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image.

Graph Generation Scene Graph Generation

Universal Correspondence Network

no code implementations NeurIPS 2016 Christopher B. Choy, JunYoung Gwak, Silvio Savarese, Manmohan Chandraker

We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations.

Metric Learning Semantic Similarity +1

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

11 code implementations2 Apr 2016 Christopher B. Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, Silvio Savarese

Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).

3D Object Reconstruction 3D Reconstruction

Cannot find the paper you are looking for? You can Submit a new open access paper.