In recent years, autonomous driving has garnered escalating attention for its
potential to relieve drivers' burdens and improve driving safety. Vision-based
3D occupancy prediction, which predicts the spatial occupancy status and
semantics of 3D voxel grids around the autonomous vehicle from image inputs, is
an emerging perception task suitable for cost-effective perception system of
autonomous driving. Although numerous studies have demonstrated the greater
advantages of 3D occupancy prediction over object-centric perception tasks,
there is still a lack of a dedicated review focusing on this rapidly developing
field. In this paper, we first introduce the background of vision-based 3D
occupancy prediction and discuss the challenges in this task. Secondly, we
conduct a comprehensive survey of the progress in vision-based 3D occupancy
prediction from three aspects: feature enhancement, deployment friendliness and
label efficiency, and provide an in-depth analysis of the potentials and
challenges of each category of methods. Finally, we present a summary of
prevailing research trends and propose some inspiring future outlooks. To
provide a valuable reference for researchers, a regularly updated collection of
related papers, datasets, and codes is organized at
this https URL

自动驾驶领域的一项新兴感知任务是基于视觉的 3D 占用状态预测，该论文对其背景、挑战、研究进展和未来展望进行了综述，并提供了与该主题相关的论文、数据集和代码的收集。

自主驾驶中基于视觉的三维占用预测：综述与展望

Vision-based 3D occupancy prediction in autonomous driving: a review and  outlook

Autonomous UAV racing has recently emerged as an interesting research
problem. The dream is to beat humans in this new fast-paced sport. A common
approach is to learn an end-to-end policy that directly predicts controls from
raw images by imitating an expert. However, such a policy is limited by the
expert it imitates and scaling to other environments and vehicle dynamics is
difficult. One approach to overcome the drawbacks of an end-to-end policy is to
train a network only on the perception task and handle control with a PID or
MPC controller. However, a single controller must be extensively tuned and
cannot usually cover the whole state space. In this paper, we propose learning
an optimized controller using a DNN that fuses multiple controllers. The
network learns a robust controller with online trajectory filtering, which
suppresses noisy trajectories and imperfections of individual controllers. The
result is a network that is able to learn a good fusion of filtered
trajectories from different controllers leading to significant improvements in
overall performance. We compare our trained network to controllers it has
learned from, end-to-end baselines and human pilots in a realistic simulation;
our network beats all baselines in extensive experiments and approaches the
performance of a professional human pilot. A video summarizing this work is
available at this https URL

本文提出了一种学习使用深度神经网络 (DNN) 融合多个控制器的优化控制器的方法，该网络使用在线轨迹过滤，能够学习过滤后的来自不同控制器的轨迹的好融合，从而在自主 UAV 比赛中取得显著的表现优势。