Papers › Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training

Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training

3 Nov 2023CVPR 2024 1arXiv:2311.01734archive 2025-07-28

Yipeng Gao, Zeyu Wang, Wei-Shi Zheng, Cihang Xie, Yuyin Zhou

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.

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Tasks

Contrastive LearningRetrievalText to 3DZero-Shot Transfer 3D Point Cloud ClassificationZero-shot 3D classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Zero-Shot Transfer 3D Point Cloud Classification ModelNet40 MixCon3D-PointBERT Accuracy (%) 86.8 #4 of 16 Archive leaderboard report
Zero-Shot Transfer 3D Point Cloud Classification ScanObjectNN MixCon3D-PointBERT OBJ_ONLY Accuracy(%) 58.6 #6 of 10 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Contrastive Learning

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