Computer vision techniques play a central role in the perception stack of
autonomous vehicles. Such methods are employed to perceive the vehicle
surroundings given sensor data. 3D LiDAR sensors are commonly used to collect
sparse 3D point clouds from the scene. However, compared to human perception,
such systems struggle to deduce the unseen parts of the scene given those
sparse point clouds. In this matter, the scene completion task aims at
predicting the gaps in the LiDAR measurements to achieve a more complete scene
representation. Given the promising results of recent diffusion models as
generative models for images, we propose extending them to achieve scene
completion from a single 3D LiDAR scan. Previous works used diffusion models
over range images extracted from LiDAR data, directly applying image-based
diffusion methods. Distinctly, we propose to directly operate on the points,
reformulating the noising and denoising diffusion process such that it can
efficiently work at scene scale. Together with our approach, we propose a
regularization loss to stabilize the noise predicted during the denoising
process. Our experimental evaluation shows that our method can complete the
scene given a single LiDAR scan as input, producing a scene with more details
compared to state-of-the-art scene completion methods. We believe that our
proposed diffusion process formulation can support further research in
diffusion models applied to scene-scale point cloud data.

使用扩散模型方法在单个 3D LiDAR 扫描中完成场景实现了比最先进的完成场景方法更多细节的场景，为扩散模型应用于场景规模点云数据的进一步研究提供了支持。

实现实际 3D LiDAR 场景补全的扩展扩散模型

Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion

With the democratization of 3D LiDAR sensors, precise LiDAR odometries and
SLAM are in high demand. New methods regularly appear, proposing solutions
ranging from small variations in classical algorithms to radically new
paradigms based on deep learning. Yet it is often difficult to compare these
methods, notably due to the few datasets on which the methods can be evaluated
and compared. Furthermore, their weaknesses are rarely examined, often letting
the user discover the hard way whether a method would be appropriate for a use
case. In this paper, we review and organize the main 3D LiDAR odometries into
distinct categories. We implemented several approaches (geometric based, deep
learning based, and hybrid methods) to conduct an in-depth analysis of their
strengths and weaknesses on multiple datasets, guiding the reader through the
different LiDAR odometries available. Implementation of the methods has been
made publicly available at this https URL

本文针对近年来高精度三维激光雷达及其在 SLAM 应用中的需求，总结和分类了主要的三维激光雷达里程计算法，对比了基于几何和深度学习的方法在多个数据集上的优缺点。

我的 LiDAR 里程计工具箱有哪些？

What's in My LiDAR Odometry Toolbox?

Point cloud data from 3D LiDAR sensors are one of the most crucial sensor
modalities for versatile safety-critical applications such as self-driving
vehicles. Since the annotations of point cloud data is an expensive and
time-consuming process, therefore recently the utilisation of simulated
environments and 3D LiDAR sensors for this task started to get some popularity.
With simulated sensors and environments, the process for obtaining an annotated
synthetic point cloud data became much easier. However, the generated synthetic
point cloud data are still missing the artefacts usually exist in point cloud
data from real 3D LiDAR sensors. As a result, the performance of the trained
models on this data for perception tasks when tested on real point cloud data
is degraded due to the domain shift between simulated and real environments.
Thus, in this work, we are proposing a domain adaptation framework for bridging
this gap between synthetic and real point cloud data. Our proposed framework is
based on the deep cycle-consistent generative adversarial networks (CycleGAN)
architecture. We have evaluated the performance of our proposed framework on
the task of vehicle detection from a bird's eye view (BEV) point cloud images
coming from real 3D LiDAR sensors. The framework has shown competitive results
with an improvement of more than 7% in average precision score over other
baseline approaches when tested on real BEV point cloud images.

本研究通过提出基于 CycleGAN 架构的域自适应框架，在从真实 3D LiDAR 传感器获取的鸟瞰（BEV）点云图像上检测车辆时，将合成的点云数据与真实点云数据之间的领域转移缩小，相对于常规方法提高了超过 7% 的平均精确度分数。