9 papers · 1 filter
Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching
Junpeng Jing, Ronglai Zuo, Zhelun Shen +5
Recent advances in stereo matching have achieved remarkable accuracy, but often rely on large models, heavy computation, or additional foundation-model priors, making them difficul…
RGB-Pointmap Pretraining for Unified 3D Scene Understanding
Ye Mao, Weixun Luo, Ranran Huang +2
Pretraining 3D encoders through alignment with Contrastive Language-Image Pre-training (CLIP) has emerged as a promising direction for learning generalizable representations for 3D…
POMA-3D: The Point Map Way to 3D Scene Understanding
Ye Mao, Weixun Luo, Ranran Huang +2
In this paper, we introduce POMA-3D, the first self-supervised 3D representation model learned from point maps. Point maps encode explicit 3D coordinates on a structured 2D grid, p…
Lite Any Stereo: Efficient Zero-Shot Stereo Matching
Junpeng Jing, Weixun Luo, Ye Mao +1
Recent advances in stereo matching have focused on accuracy, often at the cost of significantly increased model size. Traditionally, the community has regarded efficient models as…
Stereo Any Video: Temporally Consistent Stereo Matching
Junpeng Jing, Weixun Luo, Ye Mao +1
This paper introduces Stereo Any Video, a powerful framework for video stereo matching. It can estimate spatially accurate and temporally consistent disparities without relying on…
Hypo3D: Exploring Hypothetical Reasoning in 3D
Ye Mao, Weixun Luo, Junpeng Jing +2
The rise of vision-language foundation models marks an advancement in bridging the gap between human and machine capabilities in 3D scene reasoning. Existing 3D reasoning benchmark…