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20232026
most citedAutonomous Driving in Unstructured Environments: How Far Have We Come?

6 citations · 11 across the 11 of their papers we have counts for

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cs.CV2024★ 1 cited

Dust to Tower: Coarse-to-Fine Photo-Realistic Scene Reconstruction from Sparse Uncalibrated Images

Xudong Cai, Yongcai Wang, Zhaoxin Fan +7

Photo-realistic scene reconstruction from sparse-view, uncalibrated images is highly required in practice. Although some successes have been made, existing methods are either Spars…

cs.CV2024

MambaVO: Deep Visual Odometry Based on Sequential Matching Refinement and Training Smoothing

Shuo Wang, Wanting Li, Yongcai Wang +5

Deep visual odometry has demonstrated great advancements by learning-to-optimize technology. This approach heavily relies on the visual matching across frames. However, ambiguous m…

cs.RO2024★ 6 cited

Autonomous Driving in Unstructured Environments: How Far Have We Come?

Chen Min, Shubin Si, Xu Wang +14

Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene co…

cs.CV2024

VSFormer: Mining Correlations in Flexible View Set for Multi-view 3D Shape Understanding

Hongyu Sun, Yongcai Wang, Peng Wang +3

View-based methods have demonstrated promising performance in 3D shape understanding. However, they tend to make strong assumptions about the relations between views or learn the m…

cs.CV2024★ 3 cited

PRISM: PRogressive dependency maxImization for Scale-invariant image Matching

Xudong Cai, Yongcai Wang, Lun Luo +5

Image matching aims at identifying corresponding points between a pair of images. Currently, detector-free methods have shown impressive performance in challenging scenarios, thank…

cs.CV2024

VOLoc: Visual Place Recognition by Querying Compressed Lidar Map

Xudong Cai, Yongcai Wang, Zhe Huang +2

The availability of city-scale Lidar maps enables the potential of city-scale place recognition using mobile cameras. However, the city-scale Lidar maps generally need to be compre…