In this paper, we introduce the task of multi-view RGB-based 3D object
detection as an end-to-end optimization problem. To address this problem, we
propose ImVoxelNet, a novel fully convolutional method of 3D object detection
based on monocular or multi-view RGB images. The number of monocular images in
each multi-view input can variate during training and inference; actually, this
number might be unique for each multi-view input. ImVoxelNet successfully
handles both indoor and outdoor scenes, which makes it general-purpose.
Specifically, it achieves state-of-the-art results in car detection on KITTI
(monocular) and nuScenes (multi-view) benchmarks among all methods that accept
RGB images. Moreover, it surpasses existing RGB-based 3D object detection
methods on the SUN RGB-D dataset. On ScanNet, ImVoxelNet sets a new benchmark
for multi-view 3D object detection. The source code and the trained models are
available at this https URL

本文介绍了通过多视角 RGB 图像完成 3D 对象检测的方法，并提出了一种全新的完全卷积方法 ImVoxelNet，它在 KITTI（单视角）和 nuScenes（多视角）基准测试以及 SUN RGB-D 数据集上均取得了业内领先的效果，在 ScanNet 上创造了新的多视角 3D 对象检测基准。