Safety-critical 3D scene understanding tasks necessitate not only accurate
but also confident predictions from 3D perception models. This study introduces
Calib3D, a pioneering effort to benchmark and scrutinize the reliability of 3D
scene understanding models from an uncertainty estimation viewpoint. We
comprehensively evaluate 28 state-of-the-art models across 10 diverse 3D
datasets, uncovering insightful phenomena that cope with both the aleatoric and
epistemic uncertainties in 3D scene understanding. We discover that despite
achieving impressive levels of accuracy, existing models frequently fail to
provide reliable uncertainty estimates -- a pitfall that critically undermines
their applicability in safety-sensitive contexts. Through extensive analysis of
key factors such as network capacity, LiDAR representations, rasterization
resolutions, and 3D data augmentation techniques, we correlate these aspects
directly with the model calibration efficacy. Furthermore, we introduce DeptS,
a novel depth-aware scaling approach aimed at enhancing 3D model calibration.
Extensive experiments across a wide range of configurations validate the
superiority of our method. We hope this work could serve as a cornerstone for
fostering reliable 3D scene understanding. Code and benchmark toolkits are
publicly available.

对现有的三维场景理解模型进行不确定性估计并评估其可靠性，发现在安全敏感的环境中，虽然现有模型在准确性方面表现出色，但却经常无法提供可靠的不确定性估计，这严重影响了它们的适用性。通过分析网络容量、LiDAR 表示、光栅化分辨率和三维数据增强技术等关键因素，将这些方面直接与模型的校准效果相关联，并引入了一种新的深度感知缩放方法来增强三维模型的校准。在各种配置下进行的大量实验证实了我们方法的优越性。希望这项工作能够成为可靠的三维场景理解的基石。提供了代码和基准工具包。