The task of estimating 3D occupancy from surrounding-view images is an exciting development in the field of autonomous driving, following the success of Bird's Eye View (BEV) perception. This task provides crucial 3D attributes of the driving environment, enhancing the overall understanding and perception of the surrounding space. In this work, we present a simple framework for 3D occupancy estimation, which is a CNN-based framework designed to reveal several key factors for 3D occupancy estimation, such as network design, optimization, and evaluation. In addition, we explore the relationship between 3D occupancy estimation and other related tasks, such as monocular depth estimation and 3D reconstruction, which could advance the study of 3D perception in autonomous driving. For evaluation, we propose a simple sampling strategy to define the metric for occupancy evaluation, which is flexible for current public datasets. Moreover, we establish the benchmark in terms of the depth estimation metric, where we compare our proposed method with monocular depth estimation methods on the DDAD and Nuscenes datasets and achieve competitive performance. The relevant code will be updated in https://github.com/GANWANSHUI/SimpleOccupancy.

翻译：从环视图像中估计3D占据信息是自动驾驶领域继鸟瞰图感知取得成功之后的一项令人振奋的发展。该任务提供了驾驶环境的关键3D属性，增强了周围空间的整体理解与感知。本文提出一个基于CNN的简易3D占据估计框架，旨在揭示网络设计、优化与评估等若干关键因素。此外，我们探索了3D占据估计与单目深度估计、3D重建等相关任务之间的关系，这将推动自动驾驶3D感知研究的发展。在评估方面，我们提出一种简洁的采样策略来定义占据评估指标，该策略灵活适用于当前公开数据集。同时，我们基于深度估计度量建立了基准，在DDAD和Nuscenes数据集上将所提方法与单目深度估计方法进行对比，并取得了具有竞争力的性能。相关代码将更新于https://github.com/GANWANSHUI/SimpleOccupancy。