17 citations · 23 across the 5 of their papers we have counts for
6 papers
Scene Synthesis from Human Motion
Sifan Ye, Yixing Wang, Jiaman Li +4
Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly. Meanwhile, human motion alone contains rich info…
Depth Is All You Need for Monocular 3D Detection
Dennis Park, Jie Li, Dian Chen +2
A key contributor to recent progress in 3D detection from single images is monocular depth estimation. Existing methods focus on how to leverage depth explicitly, by generating pse…
Shadows Shed Light on 3D Objects
Ruoshi Liu, Sachit Menon, Chengzhi Mao +3
3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a…
Revealing Occlusions with 4D Neural Fields
Basile Van Hoorick, Purva Tendulkar, Didac Suris +3
For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence. We introduce a framework for learning to estimat…
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
Aditya Ganeshan, Alexis Vallet, Yasunori Kudo +5
Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets. Labelling is a tedious process that can take hours per image. Automatica…
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Dennis Park, Rares Ambrus, Vitor Guizilini +2
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors. These…