most citedLearning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance

9 citations · 9 across the 2 of their papers we have counts for

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cs.CV2025

Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views

Jiang Wu, Rui Li, Yu Zhu +3

We present a Gaussian Splatting method for surface reconstruction using sparse input views. Previous methods relying on dense views struggle with extremely sparse Structure-from-Mo…

cs.CV20241 cited

GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo

Jiang Wu, Rui Li, Haofei Xu +4

Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However, directly aggregating adjacent costs can lead to suboptimal results due to…

cs.CV20241 cited

Boosting Multi-view Stereo with Late Cost Aggregation

Jiang Wu, Rui Li, Yu Zhu +3

Pairwise matching cost aggregation is a crucial step for modern learning-based Multi-view Stereo (MVS). Prior works adopt an early aggregation scheme, which adds up pairwise costs…

cs.CV2023

Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes

Rui Li, Dong Gong, Wei Yin +6

Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consis…

cs.CV20219 cited

Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance

Rui Li, Xiantuo He, Danna Xue +5

Self-supervised depth estimation has made a great success in learning depth from unlabeled image sequences. While the mappings between image and pixel-wise depth are well-studied i…