Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection
This work addresses the high cost of 3D data annotation for autonomous driving and robotics by leveraging existing image models to improve detection with limited labels, representing an incremental advance in semi-supervised learning for 3D perception.
The paper tackles the problem of expensive 3D bounding box annotation for object detection by proposing a shelf-supervised cross-modal pre-training method that uses off-the-shelf image foundation models to generate pseudo-labels from paired RGB and LiDAR data, resulting in significantly better semi-supervised detection accuracy on nuScenes and WOD datasets compared to prior self-supervised methods.
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