Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection

• URL: http://arxiv.org/abs/2406.10115v3
• Date: Tue, 15 Oct 2024 14:54:01 GMT
• Title: Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection
• Authors: Mehar Khurana, Neehar Peri, James Hays, Deva Ramanan,
• Abstract summary: State-of-the-art 3D object detectors are often trained on massive labeled datasets. Recent works demonstrate that self-supervised pre-training with unlabeled data can improve detection accuracy with limited labels. We propose a shelf-supervised approach for generating zero-shot 3D bounding boxes from paired RGB and LiDAR data.
• Score: 52.66283064389691
• License:
• Abstract: State-of-the-art 3D object detectors are often trained on massive labeled datasets. However, annotating 3D bounding boxes remains prohibitively expensive and time-consuming, particularly for LiDAR. Instead, recent works demonstrate that self-supervised pre-training with unlabeled data can improve detection accuracy with limited labels. Contemporary methods adapt best-practices for self-supervised learning from the image domain to point clouds (such as contrastive learning). However, publicly available 3D datasets are considerably smaller and less diverse than those used for image-based self-supervised learning, limiting their effectiveness. We do note, however, that such 3D data is naturally collected in a multimodal fashion, often paired with images. Rather than pre-training with only self-supervised objectives, we argue that it is better to bootstrap point cloud representations using image-based foundation models trained on internet-scale data. Specifically, we propose a shelf-supervised approach (e.g. supervised with off-the-shelf image foundation models) for generating zero-shot 3D bounding boxes from paired RGB and LiDAR data. Pre-training 3D detectors with such pseudo-labels yields significantly better semi-supervised detection accuracy than prior self-supervised pretext tasks. Importantly, we show that image-based shelf-supervision is helpful for training LiDAR-only, RGB-only and multi-modal (RGB + LiDAR) detectors. We demonstrate the effectiveness of our approach on nuScenes and WOD, significantly improving over prior work in limited data settings. Our code is available at https://github.com/meharkhurana03/cm3d

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