论文标题
rangercnn：迈向使用范围图像表示的快速准确的3D对象检测
RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation
论文作者
论文摘要
我们提出了Rangercnn，这是一种基于范围图像表示的新颖有效的3D对象检测框架。大多数现有方法是基于体素的或基于点的。尽管已经引入了几种优化，以缓解稀疏性问题并加快运行时间的速度，但两种表示在计算上仍然效率低下。与它们相比，范围图像表示是密集和紧凑的，可以利用强大的2D卷积。即便如此，由于比例变化和遮挡，在3D对象检测中也不是首选范围图像。在本文中，我们利用扩张的残留块（DRB）更好地适应不同的物体尺度并获得更灵活的接收场。考虑到尺度变化和遮挡，我们提出了RV-PV-BEV（范围视图视图鸟视图）模块将功能从RV传输到BEV。锚定在BEV中定义，避免了比例变化和遮挡。 RV和BEV都无法提供足够的信息以估算高度；因此，我们提出了一个两阶段的RCNN，以提高3D检测性能。上述点视图不仅是从RV到BEV的桥梁，而且还为RCNN提供了点。实验表明，Rangercnn在Kitti数据集和Waymo Open数据集上实现了最新的性能，并为实时3D对象检测提供了更多可能性。我们进一步介绍并讨论了基于范围图像的方法的数据增强策略，这对于未来对范围图像的研究非常有价值。
We present RangeRCNN, a novel and effective 3D object detection framework based on the range image representation. Most existing methods are voxel-based or point-based. Though several optimizations have been introduced to ease the sparsity issue and speed up the running time, the two representations are still computationally inefficient. Compared to them, the range image representation is dense and compact which can exploit powerful 2D convolution. Even so, the range image is not preferred in 3D object detection due to scale variation and occlusion. In this paper, we utilize the dilated residual block (DRB) to better adapt different object scales and obtain a more flexible receptive field. Considering scale variation and occlusion, we propose the RV-PV-BEV (range view-point view-bird's eye view) module to transfer features from RV to BEV. The anchor is defined in BEV which avoids scale variation and occlusion. Neither RV nor BEV can provide enough information for height estimation; therefore, we propose a two-stage RCNN for better 3D detection performance. The aforementioned point view not only serves as a bridge from RV to BEV but also provides pointwise features for RCNN. Experiments show that RangeRCNN achieves state-of-the-art performance on the KITTI dataset and the Waymo Open dataset, and provides more possibilities for real-time 3D object detection. We further introduce and discuss the data augmentation strategy for the range image based method, which will be very valuable for future research on range image.
