3D object detection is a core perceptual challenge for robotics and autonomous driving. However, the class-taxonomies in modern autonomous driving datasets are significantly smaller than many influential 2D detection datasets. In this work, we address the long-tail problem by leveraging both the large class-taxonomies of modern 2D datasets and the robustness of state-of-the-art 2D detection methods. We proceed to mine a large, unlabeled dataset of images and LiDAR, and estimate 3D object bounding cuboids, seeded from an off-the-shelf 2D instance segmentation model. Critically, we constrain this ill-posed 2D-to-3D mapping by using high-definition maps and object size priors. The result of the mining process is 3D cuboids with varying confidence. This mining process is itself a 3D object detector, although not especially accurate when evaluated as such. However, we then train a 3D object detection model on these cuboids, consistent with other recent observations in the deep learning literature, we find that the resulting model is fairly robust to the noisy supervision that our mining process provides. We mine a collection of 1151 unlabeled, multimodal driving logs from an autonomous vehicle and use the discovered objects to train a LiDAR-based object detector. We show that detector performance increases as we mine more unlabeled data. With our full, unlabeled dataset, our method performs competitively with fully supervised methods, even exceeding the performance for certain object categories, without any human 3D annotations.
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Massachusetts Institute of Technology（麻省理工学院）；South China University of Technology（华南理工大学）；Tsinghua University（清华大学）；Shanghai Qi Zhi Institute（上海期智研究院）
Transferable Physical Attack against Object Detection with Separable Attention
National University of Defense Technology（国防科技大学）；Defense Innovation Institute（国防创新研究院）；Chinese Academy of Military Science（中国人民解放军军事科学院）；Anhui University of Technology（安徽工业大学）
Learning 3D Scene Analogies with Neural Contextual Scene Maps
Dept. of Electrical and Computer Engineering, Seoul National University（电子与计算机工程系，首尔国立大学）；Interdisciplinary Program in Artificial Intelligence and INMC, Seoul National University（人工智能跨学科项目及INMC，首尔国立大学）
