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publications
SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration
In this paper, we propose a novel method termed SPEAL to leverage skeletal representations for effective learning of intrinsic topologies of point clouds, facilitating robust capture of geometric intricacy.
Citation
Xiong, Kezheng, et al. "SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 6. 2024.
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
In this paper, we propose a novel unsupervised registration method termed INTEGER to incorporate high-level contextual information for reliable pseudo-label mining.
Citation
Xiong, Kezheng, et al. "Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration." The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024).
TACO: Task-Aware Contrastive Learning for Joint LiDAR Localization and 3D Object Detection
This paper proposes TACO, the first Task-Aware COntrastive learning framework, which performs joint LiDAR localization and 3D object detection within a single, unified network.
Citation
Leyuan, Xing, et al. "TACO: Task-Aware Contrastive Learning for Joint LiDAR Localization and 3D Object Detection", In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Unsupervised Point Cloud Registration via Training-Time Semantic Guidance
We propose CAESAR, a teacher-student framework guided by an off-the-shelf 3D segmentation model exclusively during training.
Citation
Xiong, K., Xu, S., Ao, S., Shen, S., Wang, C., Wen, C. (2026). Unsupervised Point Cloud Registration via Training-Time Semantic Guidance. In: Favaro, P., Kukelova, Z., Maki, A., Rohrbach, A., Schindler, K., Tombari, F. (eds) Computer Vision – ECCV 2026. ECCV 2026. Lecture Notes in Computer Science, vol 17035. Springer, Cham. https://doi.org/10.1007/978-3-032-37464-6_26
Cross-Modal Prior-Guided Training with Visual Foundation Models for Unsupervised LiDAR Point Cloud Registration
We introduce PROMOTER, a novel method leveraging Visual Foundation Models for unsupervised learning of LiDAR point cloud registration, which is capable of training various registration models and scales well with stronger VFMs.
Citation
Coming Soon
MoT3DVG: A Benchmark for Outdoor 3D Visual Grounding with Motion-Aware Descriptions and Temporal Cues
We therefore introduce MoT3DVG, a large-scale dataset for dynamic-aware outdoor 3DVG with temporally evolving motion descriptions. It contains 850 scenarios with 31,128 frames from nuScenes dataset, and provides 144,568 language prompts with motion-aware descriptions for dynamic objects across time. We further propose DynaVG, a novel framework that effectively leverages temporal cues for outdoor 3DVG.
Citation
Coming Soon
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Data Structures (Teaching Assistant)
Undergraduate course, Xiamen University, 2024
Supporting undergraduate students in mastering fundamental data structures and algorithms. Conducting lab sessions, office hours, and exam preparation for 90+ students in C++ programming and algorithmic thinking.