Segment any point cloud sequences by distilling vision foundation models

Y Liu, L Kong, J Cen, R Chen… - Advances in …, 2024 - proceedings.neurips.cc
Recent advancements in vision foundation models (VFMs) have opened up new
possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a …

Multi-Space Alignments Towards Universal LiDAR Segmentation

Y Liu, L Kong, X Wu, R Chen, X Li… - Proceedings of the …, 2024 - openaccess.thecvf.com
A unified and versatile LiDAR segmentation model with strong robustness and
generalizability is desirable for safe autonomous driving perception. This work presents …

4d contrastive superflows are dense 3d representation learners

X Xu, L Kong, H Shuai, W Zhang, L Pan, K Chen… - … on Computer Vision, 2025 - Springer
In the realm of autonomous driving, accurate 3D perception is the foundation. However,
developing such models relies on extensive human annotations–a process that is both …

[PDF][PDF] Semantic Sample Mixing Based Unsupervised Domain Adaptation for Semantic Segmentation of 3D LiDAR Point Clouds

A Nihal, V Kumar - researchgate.net
Semantic Sample Mixing Based Unsupervised Domain Adaptation for Semantic Segmentation
of 3D LiDAR Point Clouds Page 1 Semantic Sample Mixing Based Unsupervised Domain …