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SPOT: Scalable 3D Pre-Training via Occupancy Prediction for Learning Transferable 3D Representations.

Xiangchao Yan, Runjian Chen, Bo Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 8, 2025
    PubMed
    Summary

    Scalable Pre-training via Occupancy prediction (SPOT) enables efficient 3D representation learning for LiDAR point clouds. This approach reduces labeling burden and improves performance across diverse tasks and domains.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Robotics

    Background:

    • Annotating 3D LiDAR point clouds for perception is crucial but labor-intensive.
    • Pretraining-finetuning methods can reduce labeling efforts for downstream tasks.

    Purpose of the Study:

    • Introduce SPOT (Scalable Pre-training via Occupancy prediction) for learning transferable 3D representations.
    • Demonstrate the effectiveness of SPOT in a label-efficient fine-tuning paradigm.

    Main Methods:

    • Developed SPOT, a pre-training strategy using occupancy prediction for transferable 3D representations.
    • Implemented beam re-sampling for point cloud augmentation and a class-balancing strategy to address domain gaps.
    • Investigated scalable pre-training with increasing amounts of pre-training data.

    Main Results:

    • SPOT shows effectiveness across various public datasets and downstream tasks.
    • Demonstrated general representation power, cross-domain robustness, and data scalability.
    • Confirmed that more pre-training data leads to better downstream performance.
    • Showcased compatibility with unlabeled data.

    Conclusions:

    • Occupancy prediction is a viable task for general representation learning in 3D point clouds.
    • SPOT facilitates understanding of LiDAR data and advances LiDAR pre-training.
    • The proposed methods address domain gaps and enhance the scalability of pre-training strategies.