{"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/mixcon3d-synergizing-multi-view-and-cross","title":"Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training","arxiv_id":"2311.01734","date":"2023-11-03","proceeding":"CVPR 2024 1","authors":["Yipeng Gao","Zeyu Wang","Wei-Shi Zheng","Cihang Xie","Yuyin Zhou"],"abstract":"Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i.e., aligning point cloud representation to image and text embedding space individually. In this paper, we introduce MixCon3D, a simple yet effective method aiming to sculpt holistic 3D representation in contrastive language-image-3D pre-training. In contrast to point cloud only, we develop the 3D object-level representation from complementary perspectives, e.g., multi-view rendered images with the point cloud. Then, MixCon3D performs language-3D contrastive learning, comprehensively depicting real-world 3D objects and bolstering text alignment. Additionally, we pioneer the first thorough investigation of various training recipes for the 3D contrastive learning paradigm, building a solid baseline with improved performance. Extensive experiments conducted on three representative benchmarks reveal that our method significantly improves over the baseline, surpassing the previous state-of-the-art performance on the challenging 1,156-category Objaverse-LVIS dataset by 5.7%. The versatility of MixCon3D is showcased in applications such as text-to-3D retrieval and point cloud captioning, further evidencing its efficacy in diverse scenarios. The code is available at https://github.com/UCSC-VLAA/MixCon3D.","url_abs":"https://arxiv.org/abs/2311.01734v2","url_pdf":"https://arxiv.org/pdf/2311.01734v2.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":"mixcon3d-synergizing-multi-view-and-cross","repo_url":"https://github.com/ucsc-vlaa/mixcon3d","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-to-3d","task_name":"Text to 3D"},{"task_slug":"zero-shot-transfer-3d-point-cloud","task_name":"Zero-Shot Transfer 3D Point Cloud Classification"},{"task_slug":"zero-shot-3d-classification","task_name":"Zero-shot 3D classification"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/zero-shot-transfer-3d-point-cloud","task":"Zero-Shot Transfer 3D Point Cloud Classification","dataset":"ModelNet40","model":"MixCon3D-PointBERT","rank_in_archive_order":4,"of":16,"metrics":{"Accuracy (%)":"86.8"},"uses_additional_data":true},{"leaderboard":"/sota/zero-shot-transfer-3d-point-cloud-2","task":"Zero-Shot Transfer 3D Point Cloud Classification","dataset":"ScanObjectNN","model":"MixCon3D-PointBERT","rank_in_archive_order":6,"of":10,"metrics":{"OBJ_ONLY Accuracy(%)":"58.6"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.01734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01734"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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