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20232026
most citedAdaptive Multi-Modal Cross-Entropy Loss for Stereo Matching

2 citations · 2 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement

Zhihao Yang, Zhiyu Xiang, Peng Xu +5

V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative agents. However,…

cs.CV2025

MAGIC: Learning from Visibility Asymmetry for Unsupervised Stereo Matching

Peng Xu, Zhiyu Xiang, Tingming Bai +5

Learning disparity in occluded regions remains difficult for unsupervised stereo matching. Photometric supervision lacks valid target-view correspondences in these regions, while t…

cs.CV2025

CVFusion: Cross-View Fusion of 4D Radar and Camera for 3D Object Detection

Hanzhi Zhong, Zhiyu Xiang, Ruoyu Xu +6

4D radar has received significant attention in autonomous driving thanks to its robustness under adverse weathers. Due to the sparse points and noisy measurements of the 4D radar,…

cs.CV2025

MIDAS: Modeling Ground-Truth Distributions with Dark Knowledge for Domain Generalized Stereo Matching

Peng Xu, Zhiyu Xiang, Jingyun Fu +3

Despite the significant advances in domain generalized stereo matching, existing methods still exhibit domain-specific preferences when transferring from synthetic to real domains,…

cs.CV2024

SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection

Ruoyu Xu, Zhiyu Xiang, Chenwei Zhang +6

3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor i…

cs.CV2023★ 2 cited

Adaptive Multi-Modal Cross-Entropy Loss for Stereo Matching

Peng Xu, Zhiyu Xiang, Chenyu Qiao +2

Despite the great success of deep learning in stereo matching, recovering accurate disparity maps is still challenging. Currently, L1 and cross-entropy are the two most widely used…