# Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation Source: [Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:52:52.049Z Authors: Jingyu Zhang, Huitong Yang, Dai-Jie Wu, Jacky Keung, Xuesong Li, Xinge Zhu, Yuexin Ma Published: 2023-09-19T14:29:57.000Z ## Abstract Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations. A natural option is to explore the unsupervised methodology for 3D perception tasks. However, such methods often face substantial performance-drop difficulties. Fortunately, we found that there exist amounts of image-based datasets and an alternative can be proposed, i.e., transferring the knowledge in the 2D images to 3D point clouds. Specifically, we propose a novel approach for the challenging cross-modal and cross-domain adaptation task by fully exploring the relationship between images and point clouds and designing effective feature alignment strategies. Without any 3D labels, our method achieves state-of-the-art performance for 3D point cloud semantic segmentation on SemanticKITTI by using the knowledge of KITTI360 and GTA5, compared to existing unsupervised and weakly-supervised baselines. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()