Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quantitative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work.
翻译:自动驾驶被认为是防止人类遭受严重撞车事故的最有希望的补救办法之一。为此目的,3D物体探测是感知堆堆的核心基础,特别是为了路途规划、运动预测和避免碰撞等目的。我们快速审视我们所取得的进展,把在图像缺乏深度信息、从部分隐蔽的无结构云层中学习以及不同模式特征的语义一致的情况下,视觉外观恢复的挑战归结为挑战。尽管作出了现有努力,但自动驾驶的3D物体探测仍然处于萌芽阶段。最近,对大量文献进行了调查,以完成这一3D远景任务。然而,几乎没有调查调查研究如何收集和构建这一不断增长的知识。因此,我们的目标是在一项全面调查中填补这一空白,涵盖所有主要关切,包括传感器、数据集、性能度度和最新的最新最新探测方法,以及其利弊。此外,我们提供了与艺术状况的定量比较。我们介绍了关于15种选定代表性方法的案例研究,其中涉及运行时段分析、误差分析和稳健性分析。最后,我们在深入分析工作之后提出有希望的结论。