Lidars are depth measuring sensors widely used in autonomous driving and
augmented reality. However, the large volume of data produced by lidars can
lead to high costs in data storage and transmission. While lidar data can be
represented as two interchangeable representations: 3D point clouds and range
images, most previous work focus on compressing the generic 3D point clouds. In
this work, we show that directly compressing the range images can leverage the
lidar scanning pattern, compared to compressing the unprojected point clouds.
We propose a novel data-driven range image compression algorithm, named RIDDLE
(Range Image Deep DeLta Encoding). At its core is a deep model that predicts
the next pixel value in a raster scanning order, based on contextual laser
shots from both the current and past scans (represented as a 4D point cloud of
spherical coordinates and time). The deltas between predictions and original
values can then be compressed by entropy encoding. Evaluated on the Waymo Open
Dataset and KITTI, our method demonstrates significant improvement in the
compression rate (under the same distortion) compared to widely used point
cloud and range image compression algorithms as well as recent deep methods.

该研究提出了一种名为 RIDDLE 的数据驱动范围图像压缩算法，该算法通过深度学习模型进行预测，以更好的利用激光雷达扫描模式，相比于现有的点云和范围图像压缩方法，在相同的失真度下大幅提高了压缩速度。在 Waymo Open 数据集和 KITTI 上的测试结果显示出了显著的效果。

RIDDLE: 激光雷达数据压缩与深度范围图像 Delta 编码

RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding

This paper presents a novel 3D object detection framework that processes
LiDAR data directly on its native representation: range images. Benefiting from
the compactness of range images, 2D convolutions can efficiently process dense
LiDAR data of a scene. To overcome scale sensitivity in this perspective view,
a novel range-conditioned dilation (RCD) layer is proposed to dynamically
adjust a continuous dilation rate as a function of the measured range.
Furthermore, localized soft range gating combined with a 3D box-refinement
stage improves robustness in occluded areas, and produces overall more accurate
bounding box predictions. On the public large-scale Waymo Open Dataset, our
method sets a new baseline for range-based 3D detection, outperforming
multiview and voxel-based methods over all ranges with unparalleled performance
at long range detection.

本文提出了一个新颖的 3D 物体检测框架，可直接处理 LiDAR 原始数据；利用稠密的测距图像，通过 2D 卷积方法高效处理数据，并使用新的 range-conditioned dilation (RCD) 层根据量测距离动态调整连续膨胀率，进一步提高了检测的精度。结合 3D bounding box 细化步骤能够更好地应对遮挡区域，在 Waymo Open 数据集上表现优异，创下了新的基准。

基于范围约束的膨胀卷积用于尺度不变的三维物体检测

Range Conditioned Dilated Convolutions for Scale Invariant 3D Object  Detection

Range images captured by Time-of-Flight (ToF) cameras are corrupted with
multipath distortions due to interaction between modulated light signals and
scenes. The interaction is often complicated, which makes a model-based
solution elusive. We propose a learning-based approach for removing the
multipath distortions for a ToF camera in a robotic arm setup. Our approach is
based on deep learning. We use the robotic arm to automatically collect a large
amount of ToF range images containing various multipath distortions. The
training images are automatically labeled by leveraging a high precision
structured light sensor available only in the training time. In the test time,
we apply the learned model to remove the multipath distortions. This allows our
robotic arm setup to enjoy the speed and compact form of the ToF camera without
compromising with its range measurement errors. We conduct extensive
experimental validations and compare the proposed method to several baseline
algorithms. The experiment results show that our method achieves 55% error
reduction in range estimation and largely outperforms the baseline algorithms.

本研究提出了一种基于深度学习的学习方法，采用机械臂自动地采集大量的 ToF 范围图像，并在训练时间利用高精度结构光传感器自动标记训练图像，从而去除 ToF 相机中的多路径失真。在实验验证中，我们的方法实现了 55％的测距误差减小，并且比其他基线算法表现更好。