225 citations · 634 across the 24 of their papers we have counts for
13 papers · 1 filter
Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis
Jianqiao Wangni, Dahua Lin, Ji Liu +2
For recovering 3D object poses from 2D images, a prevalent method is to pre-train an over-complete dictionary of 3D basis poses. During testing, the detect…
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney +1
In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input…
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion
Alex Zihao Zhu, Wenxin Liu, Ziyun Wang +2
In this work, we propose a method that combines unsupervised deep learning predictions for optical flow and monocular disparity with a model based optimization procedure for instan…
Cross-Domain 3D Equivariant Image Embeddings
Carlos Esteves, Avneesh Sud, Zhengyi Luo +2
Spherical convolutional networks have been introduced recently as tools to learn powerful feature representations of 3D shapes. Spherical CNNs are equivariant to 3D rotations makin…
Labeling Panoramas with Spherical Hourglass Networks
Carlos Esteves, Kostas Daniilidis, Ameesh Makadia
With the recent proliferation of consumer-grade 360° cameras, it is worth revisiting visual perception challenges with spherical cameras given the potential benefit of their global…
Learning what you can do before doing anything
Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis +2
Intelligent agents can learn to represent the action spaces of other agents simply by observing them act. Such representations help agents quickly learn to predict the effects of t…