Calib3D：校准模型偏好以实现可靠的3D场景理解
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
AI总结：
本研究提出Calib3D基准，从不确定性估计角度评估28个模型在10个3D数据集上的可靠性，发现现有模型虽准确但不确定性估计不可靠，并提出DeptS深度感知缩放方法显著增强3D模型校准效果。
摘要：
安全关键的3D场景理解任务不仅需要3D感知模型提供准确的预测，还需要其提供有信心的预测。本研究引入了Calib3D，这是一项开创性工作，旨在从不确定性估计的角度对3D场景理解模型的可靠性进行基准测试和审查。我们在10个多样化的3D数据集上全面评估了28个最先进的模型，揭示了能够同时应对3D场景理解中偶然不确定性和认知不确定性的深刻现象。我们发现，尽管现有模型达到了令人印象深刻的准确度水平，但它们经常无法提供可靠的不确定性估计——这一隐患严重削弱了它们在安全敏感场景中的适用性。通过对网络容量、LiDAR表示、光栅化分辨率和3D数据增强技术等关键因素进行广泛分析，我们将这些方面与模型校准效能直接关联起来。此外，我们引入了DeptS，一种新颖的深度感知缩放方法，旨在增强3D模型校准。在广泛配置下的广泛实验验证了我们方法的优越性。我们希望这项工作能成为促进可靠3D场景理解的基石。代码和基准工具包已公开发布。
英文摘要：
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 toolkit are publicly available.
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