2 citations · 2 across the 3 of their papers we have counts for
4 papers
Depth Anything 3: Recovering the Visual Space from Any Views
Haotong Lin, Sili Chen, Junhao Liew +5
We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of…
Towards In-the-wild 3D Plane Reconstruction from a Single Image
Jiachen Liu, Rui Yu, Sili Chen +2
3D plane reconstruction from a single image is a crucial yet challenging topic in 3D computer vision. Previous state-of-the-art (SOTA) methods have focused on training their system…
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
Sili Chen, Hengkai Guo, Shengnan Zhu +4
Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal inconsistency in videos, hinderin…
MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction
Wang Zhao, Jiachen Liu, Sheng Zhang +5
This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane. Unlike previous robust estimator-based works (which require multiple images or…