Abstract
Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to facilitate the learning, but often fail to fully exploit the benefits of three-dimensional feature extraction in frustum and 3D space. In this paper, we propose \textbf{OccupancyM3D}, a method of learning occupancy for monocular 3D detection. It directly learns occupancy in frustum and 3D space, leading to more discriminative and informative 3D features and representations. Specifically, by using synchronized raw sparse LiDAR point clouds, we define the space status and generate voxel-based occupancy labels. We formulate occupancy prediction as a simple classification problem and design associated occupancy losses. Resulting occupancy estimates are employed to enhance original frustum/3D features. As a result, experiments on KITTI and Waymo open datasets demonstrate that the proposed method achieves a new state of the art and surpasses other methods by a significant margin. Codes and pre-trained models will be available at \url{this https URL}.
Abstract (translated)
单目3D检测是一项具有挑战性的任务，因为缺乏准确的3D信息。现有的方法通常依赖于几何约束和密集深度估计来促进学习，但往往无法 fully Exploiting 3D feature extraction in the aspect ratio and 3D space的 benefits。在本文中，我们提出了 \textbf{OccupancyM3D}，一种学习单目3D检测占用率的方法。它直接学习 aspect ratio 和 3D空间中的占用率，导致更歧视性和 informative 3D features 和表示。具体来说，通过使用同步的原始稀疏LiDAR点云，我们定义空间状态并生成以立方体表示的占用标签。我们将其作为简单的分类问题并提出相关的占用损失。结果占用估计被用于增强原始 aspect ratio 和 3D features。因此，在KITTI和 Waymo开放数据集的实验中，表明所提出的方法实现了新的先进技术，并以显著优势超越了其他方法。代码和预训练模型将可在 \url{this https URL} 上提供。
URL
https://arxiv.org/abs/2305.15694
