CLAP: Unsupervised 3D Representation Learning for Fusion 3D Perception via Curvature Sampling and Prototype Learning

• URL: http://arxiv.org/abs/2412.03059v1
• Date: Wed, 04 Dec 2024 06:26:12 GMT
• Title: CLAP: Unsupervised 3D Representation Learning for Fusion 3D Perception via Curvature Sampling and Prototype Learning
• Authors: Runjian Chen, Hang Zhang, Avinash Ravichandran, Wenqi Shao, Alex Wong, Ping Luo,
• Abstract summary: We propose CLAP, short for Curvature sampLing and swApping Prototype assignment prediction.<n>CLAP achieves 300% more performance gain as compared to previous SOTA 3D pre-training method via differentiable rendering.
• Score: 42.88303582495711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised 3D representation learning via masked-and-reconstruction with differentiable rendering is promising to reduce the labeling burden for fusion 3D perception. However, previous literature conduct pre-training for different modalities separately because of the hight GPU memory consumption. Consequently, the interaction between the two modalities (images and point clouds) is neglected during pre-training. In this paper, we explore joint unsupervised pre-training for fusion 3D perception via differentiable rendering and propose CLAP, short for Curvature sampLing and swApping Prototype assignment prediction. The contributions are three-fold. 1) To overcome the GPU memory consumption problem, we propose Curvature Sampling to sample the more informative points/pixels for pre-training. 2) We propose to use learnable prototypes to represent parts of the scenes in a common feature space and bring the idea of swapping prototype assignment prediction to learn the interaction between the two modalities. 3) To further optimize learnable prototypes, we propose an Expectation-Maximization training scheme to maximize the similarity between embeddings and prototypes, followed by a Gram Matrix Regularization Loss to avoid collapse. Experiment results on NuScenes show that CLAP achieves 300% more performance gain as compared to previous SOTA 3D pre-training method via differentiable rendering. Codes and models will be released.

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