We present an efficient 3D object detection framework based on a single RGB
image in the scenario of autonomous driving. Our efforts are put on extracting
the underlying 3D information in a 2D image and determining the accurate 3D
bounding box of the object without point cloud or stereo data. Leveraging the
off-the-shelf 2D object detector, we propose an artful approach to efficiently
obtain a coarse cuboid for each predicted 2D box. The coarse cuboid has enough
accuracy to guide us to determine the 3D box of the object by refinement. In
contrast to previous state-of-the-art methods that only use the features
extracted from the 2D bounding box for box refinement, we explore the 3D
structure information of the object by employing the visual features of visible
surfaces. The new features from surfaces are utilized to eliminate the problem
of representation ambiguity brought by only using a 2D bounding box. Moreover,
we investigate different methods of 3D box refinement and discover that a
classification formulation with quality aware loss has much better performance
than regression. Evaluated on the KITTI benchmark, our approach outperforms
current state-of-the-art methods for single RGB image based 3D object
detection.

本文提出了一种基于单个 RGB 图像的高效三维目标检测框架，旨在从二维图像中提取三维信息并在无点云或立体数据的情况下确定对象的精确三维边界框。该方法利用二维目标检测器提取表面视觉特征，消除使用二维边界框带来的表征歧义问题，并探索了不同的三维边界框细化方法，发现基于质量感知损失的分类式方法具有更好的性能。在 KITTI 基准测试上，该方法优于当前单个 RGB 图像基础的三维目标检测技术水平。