The task of estimating 3D occupancy from surrounding view images is an exciting development in the field of autonomous driving, following the success of Birds Eye View (BEV) perception.This task provides crucial 3D attributes of the driving environment, enhancing the overall understanding and perception of the surrounding space. However, there is still a lack of a baseline to define the task, such as network design, optimization, and evaluation. In this work, we present a simple attempt for 3D occupancy estimation, which is a CNN-based framework designed to reveal several key factors for 3D occupancy estimation. In addition, we explore the relationship between 3D occupancy estimation and other related tasks, such as monocular depth estimation, stereo matching, and BEV perception (3D object detection and map segmentation), which could advance the study on 3D occupancy estimation. 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 a new 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.The relevant code will be available in https://github.com/GANWANSHUI/SimpleOccupancy

翻译：从环视图像中估计三维占据信息是自动驾驶领域中一项激动人心的进展，紧随鸟瞰视角（BEV）感知的成功之后。该任务能够提供驾驶环境的关键三维属性，从而增强对周围空间的整体理解与感知能力。然而，目前仍缺乏明确定义该任务的基线方案，例如网络设计、优化及评估方法。在本工作中，我们提出了一种面向三维占据估计的简易尝试，该方案基于CNN框架，旨在揭示三维占据估计中的若干关键因素。此外，我们探讨了三维占据估计与其它相关任务（如单目深度估计、立体匹配及BEV感知（包括三维目标检测与地图分割））之间的关联，这有望推动三维占据估计研究的发展。针对评估环节，我们提出了一种简单的采样策略来定义占据估计指标，该策略可灵活适配当前公开数据集。同时，我们基于深度估计指标建立了一项新基准，将所提方法与DDAD及Nuscenes数据集上的单目深度估计方法进行了对比。相关代码将开源至https://github.com/GANWANSHUI/SimpleOccupancy