74 citations · 196 across the 26 of their papers we have counts for
4 papers · 1 filter
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…
Depth Field Networks for Generalizable Multi-view Scene Representation
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matchi…
Multi-Frame Self-Supervised Depth with Transformers
Vitor Guizilini, Rares Ambrus, Dian Chen +2
Multi-frame depth estimation improves over single-frame approaches by also leveraging geometric relationships between images via feature matching, in addition to learning appearanc…
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels
Vitor Guizilini, Kuan-Hui Lee, Rares Ambrus +1
Self-supervised monocular depth estimation enables robots to learn 3D perception from raw video streams. This scalable approach leverages projective geometry and ego-motion to lear…