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.
2 code implementations • 22 Mar 2018 • Kevin Chen, Christopher B. Choy, Manolis Savva, Angel X. Chang, Thomas Funkhouser, Silvio Savarese
To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes.
no code implementations • 20 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.
Ranked #13 on Semantic Segmentation on Semantic3D
2 code implementations • 31 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.
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.
Ranked #1 on Trajectory Prediction on PAID
5 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.
Ranked #9 on Panoptic Scene Graph Generation on PSG Dataset
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.
14 code implementations • 2 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).
Ranked #4 on 3D Reconstruction on Data3D−R2N2