{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/point-cloud-self-supervised-learning-via-3d","title":"Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder","arxiv_id":"2311.10887","date":"2023-11-17","proceeding":null,"authors":["Zhimin Chen","Yingwei Li","Longlong Jing","Liang Yang","Bing Li"],"abstract":"In recent years, the field of 3D self-supervised learning has witnessed significant progress, resulting in the emergence of Multi-Modality Masked AutoEncoders (MAE) methods that leverage both 2D images and 3D point clouds for pre-training. However, a notable limitation of these approaches is that they do not fully utilize the multi-view attributes inherent in 3D point clouds, which is crucial for a deeper understanding of 3D structures. Building upon this insight, we introduce a novel approach employing a 3D to multi-view masked autoencoder to fully harness the multi-modal attributes of 3D point clouds. To be specific, our method uses the encoded tokens from 3D masked point clouds to generate original point clouds and multi-view depth images across various poses. This approach not only enriches the model's comprehension of geometric structures but also leverages the inherent multi-modal properties of point clouds. Our experiments illustrate the effectiveness of the proposed method for different tasks and under different settings. Remarkably, our method outperforms state-of-the-art counterparts by a large margin in a variety of downstream tasks, including 3D object classification, few-shot learning, part segmentation, and 3D object detection. Code will be available at: https://github.com/Zhimin-C/Multiview-MAE","url_abs":"https://arxiv.org/abs/2311.10887v1","url_pdf":"https://arxiv.org/pdf/2311.10887v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"point-cloud-self-supervised-learning-via-3d","repo_url":"https://github.com/zhimin-c/multiview-mae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-object-classification","task_name":"3D Object Classification"},{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.10887","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}