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Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation., , , , , , and . CoRR, (2024)Robo3D: Towards Robust and Reliable 3D Perception against Corruptions., , , , , , , , and . ICCV, page 19937-19949. IEEE, (2023)CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP., , , , , , , , and . CVPR, page 7020-7030. IEEE, (2023)Optimizing LiDAR Placements for Robust Driving Perception in Adverse Conditions., , , , and . CoRR, (2024)An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models., , , , , , , , , and 3 other author(s). CoRR, (2024)Analysis of Influencing Factors on Humanoid Robots' Emotion Expressions by Body Language., , , , and . ISNN, volume 10878 of Lecture Notes in Computer Science, page 775-785. Springer, (2018)LaserMix for Semi-Supervised LiDAR Semantic Segmentation., , , and . CoRR, (2022)RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions., , , , , and . CoRR, (2023)ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation., , and . ICRA, page 9338-9345. IEEE, (2023)UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase., , , , , , , , , and 2 other author(s). ICCV, page 21605-21616. IEEE, (2023)