Scene completion and forecasting are two popular perception problems in
research for mobile agents like autonomous vehicles. Existing approaches treat
the two problems in isolation, resulting in a separate perception of the two
aspects. In this paper, we introduce a novel LiDAR perception task of Occupancy
Completion and Forecasting (OCF) in the context of autonomous driving to unify
these aspects into a cohesive framework. This task requires new algorithms to
address three challenges altogether: (1) sparse-to-dense reconstruction, (2)
partial-to-complete hallucination, and (3) 3D-to-4D prediction. To enable
supervision and evaluation, we curate a large-scale dataset termed OCFBench
from public autonomous driving datasets. We analyze the performance of closely
related existing baseline models and our own ones on our dataset. We envision
that this research will inspire and call for further investigation in this
evolving and crucial area of 4D perception. Our code for data curation and
baseline implementation is available at this https URL

本文通过介绍新颖的无人驾驶 LiDAR 感知任务 OCF，将场景完整和预测问题统一为一个相互关联的框架，解决了稀疏到稠密重建、局部到完整幻觉和 3D 到 4D 预测等三个挑战，并分析了现有模型在 OCFBench 数据集上的表现，为进一步深入研究这一关键的四维感知领域提供了启发。

基于 LiDAR 的 4D 占位完成和预测

LiDAR-based 4D Occupancy Completion and Forecasting

This technical report presents the 1st place winning solution for the Waymo
Open Dataset 3D semantic segmentation challenge 2022. Our network, termed
LidarMultiNet, unifies the major LiDAR perception tasks such as 3D semantic
segmentation, object detection, and panoptic segmentation in a single
framework. At the core of LidarMultiNet is a strong 3D voxel-based
encoder-decoder network with a novel Global Context Pooling (GCP) module
extracting global contextual features from a LiDAR frame to complement its
local features. An optional second stage is proposed to refine the first-stage
segmentation or generate accurate panoptic segmentation results. Our solution
achieves a mIoU of 71.13 and is the best for most of the 22 classes on the
Waymo 3D semantic segmentation test set, outperforming all the other 3D
semantic segmentation methods on the official leaderboard. We demonstrate for
the first time that major LiDAR perception tasks can be unified in a single
strong network that can be trained end-to-end.

LidarMultiNet 利用深度神经网络，将 3D 语义分割、物体检测和全景分割等核心 LiDAR 感知任务统一在单一框架中，实现了最佳表现。

LidarMultiNet: 一个将激光雷达语义分割、3D 目标检测和全景分割统一于单个多任务网络中的方法

LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object  Detection, and Panoptic Segmentation in a Single Multi-task Network

Scalable systems for automated driving have to reliably cope with an
open-world setting. This means, the perception systems are exposed to drastic
domain shifts, like changes in weather conditions, time-dependent aspects, or
geographic regions. Covering all domains with annotated data is impossible
because of the endless variations of domains and the time-consuming and
expensive annotation process. Furthermore, fast development cycles of the
system additionally introduce hardware changes, such as sensor types and
vehicle setups, and the required knowledge transfer from simulation. To enable
scalable automated driving, it is therefore crucial to address these domain
shifts in a robust and efficient manner. Over the last years, a vast amount of
different domain adaptation techniques evolved. There already exists a number
of survey papers for domain adaptation on camera images, however, a survey for
LiDAR perception is absent. Nevertheless, LiDAR is a vital sensor for automated
driving that provides detailed 3D scans of the vehicle's surroundings. To
stimulate future research, this paper presents a comprehensive review of recent
progress in domain adaptation methods and formulates interesting research
questions specifically targeted towards LiDAR perception.

探讨自动驾驶中的数据领域适应问题，对近年来不同的领域适应技术进行了全面的回顾并针对 LiDAR 感知提出了未来研究的有趣问题。