Inferring the 3D structure from a single image, particularly in occluded regions, remains a fundamental yet unsolved challenge in vision-centric autonomous driving. Existing unsupervised approaches typically train a neural radiance field and treat the network outputs as occupancy probabilities during evaluation, overlooking the inconsistency between training and evaluation protocols. Moreover, the prevalent use of 2D ground truth fails to reveal the inherent ambiguity in occluded regions caused by insufficient geometric constraints. To address these issues, this paper presents a reformulated benchmark for unsupervised monocular 3D occupancy prediction. We first interpret the variables involved in the volume rendering process and identify the most physically consistent representation of the occupancy probability. Building on these analyses, we improve existing evaluation protocols by aligning the newly identified representation with voxel-wise 3D occupancy ground truth, thereby enabling unsupervised methods to be evaluated in a manner consistent with that of supervised approaches. Additionally, to impose explicit constraints in occluded regions, we introduce an occlusion-aware polarization mechanism that incorporates multi-view visual cues to enhance discrimination between occupied and free spaces in these regions. Extensive experiments demonstrate that our approach not only significantly outperforms existing unsupervised approaches but also matches the performance of supervised ones. Our source code and evaluation protocol will be made available upon publication.

翻译：从单张图像推断三维结构，尤其是在遮挡区域，仍然是视觉中心自动驾驶领域一个基本但尚未解决的挑战。现有的无监督方法通常训练神经辐射场，并在评估时将网络输出视为占用概率，忽略了训练与评估协议之间的不一致性。此外，普遍使用的二维真值无法揭示因几何约束不足而在遮挡区域产生的固有模糊性。为解决这些问题，本文提出了一个重新构建的无监督单目三维占用预测基准。我们首先阐释了体渲染过程中涉及的变量，并确定了物理一致性最高的占用概率表示形式。基于这些分析，我们通过将新识别的表示形式与体素级三维占用真值对齐来改进现有评估协议，从而使无监督方法能够以与监督方法一致的方式进行评估。此外，为在遮挡区域施加显式约束，我们引入了一种遮挡感知极化机制，该机制融合多视角视觉线索以增强对这些区域中占用空间与自由空间的区分能力。大量实验表明，我们的方法不仅显著优于现有无监督方法，而且达到了与监督方法相当的性能。我们的源代码与评估协议将在论文发表时公开。