activity
20202026
most citedCross-Modality Knowledge Distillation Network for Monocular 3D Object Detection

92 citations · 145 across the 9 of their papers we have counts for

collaborators

10 papers

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

4D Gaussian Splatting SLAM

Yanyan Li, Youxu Fang, Zunjie Zhu +3

Simultaneously localizing camera poses and constructing Gaussian radiance fields in dynamic scenes establish a crucial bridge between 2D images and the 4D real world. Instead of re…

cs.CV2024★ 2 cited

UniVision: A Unified Framework for Vision-Centric 3D Perception

Yu Hong, Qian Liu, Huayuan Cheng +5

The past few years have witnessed the rapid development of vision-centric 3D perception in autonomous driving. Although the 3D perception models share many structural and conceptua…

cs.CV2023★ 2 cited

MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving

Jiale Li, Hang Dai, Hao Han +1

LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small…

cs.CV2022★ 92 cited

Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection

Yu Hong, Hang Dai, Yong Ding

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods. However, the existing met…

cs.CV2022★ 7 cited

Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

Yi-Nan Chen, Hang Dai, Yong Ding

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-ba…