This paper presents a novel 3D object detection framework that processes LiDAR data directly on a representation of the sensor's native range images. When operating in the range image view, one faces learning challenges, including occlusion and considerable scale variation, limiting the obtainable accuracy. To address these challenges, a range-conditioned dilated block (RCD) is proposed to dynamically adjust a continuous dilation rate as a function of the measured range, achieving scale invariance. Furthermore, soft range gating helps mitigate the effect of occlusion. An end-to-end trained box-refinement network brings additional performance improvements in occluded areas, and produces more accurate bounding box predictions. On the Waymo Open Dataset, currently the largest and most diverse publicly released autonomous driving dataset, our improved range-based detector outperforms state of the art at long range detection. Our framework is superior to prior multiview, voxel-based methods over all ranges, setting a new baseline for range-based 3D detection on this large scale public dataset.

中文翻译：

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用于尺度不变 3D 对象检测的范围条件扩张卷积

本文提出了一种新颖的 3D 对象检测框架，该框架直接在传感器原生范围图像的表示上处理 LiDAR 数据。在范围图像视图中操作时，人们面临着学习挑战，包括遮挡和相当大的尺度变化，从而限制了可获得的准确性。为了解决这些挑战，提出了范围条件扩张块（RCD）来动态调整连续扩张率作为测量范围的函数，从而实现尺度不变性。此外，软范围门控有助于减轻遮挡的影响。端到端训练的框细化网络在遮挡区域带来额外的性能改进，并产生更准确的边界框预测。在 Waymo 开放数据集上，目前最大、最多样化的公开发布的自动驾驶数据集，我们改进的基于距离的检测器在远程检测方面优于最先进的检测器。我们的框架在所有范围内都优于先前的多视图、基于体素的方法，为这个大规模公共数据集上的基于范围的 3D 检测设置了新的基线。