4 papers
Learning from Mistakes: Loss-Aware Memory Enhanced Continual Learning for LiDAR Place Recognition
Xufei Wang, Junqiao Zhao, Siyue Tao +3
LiDAR place recognition plays a crucial role in SLAM, robot navigation, and autonomous driving. However, existing LiDAR place recognition methods often struggle to adapt to new env…
LDRFusion: A LiDAR-Dominant multimodal refinement framework for 3D object detection
Jijun Wang, Yan Wu, Yujian Mo +3
Existing LiDAR-Camera fusion methods have achieved strong results in 3D object detection. To address the sparsity of point clouds, previous approaches typically construct spatial p…
Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection
Yujian Mo, Yan Wu, Junqiao Zhao +3
Recent advances in foundation models have opened up new possibilities for enhancing 3D perception. In particular, DepthAnything offers dense and reliable geometric priors from mono…
Ranking-aware Continual Learning for LiDAR Place Recognition
Xufei Wang, Gengxuan Tian, Junqiao Zhao +4
Place recognition plays a significant role in SLAM, robot navigation, and autonomous driving applications. Benefiting from deep learning, the performance of LiDAR place recognition…