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About this paper
Lingdong Kong, Xiang Xu, Jun Cen, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu
Researchers introduced Calib3D, the first comprehensive benchmark evaluating the reliability of uncertainty estimates in 3D scene understanding models across 10 diverse datasets and 28 state-of-the-art architectures. They also developed Depth-Aware Scaling (DeptS), a novel calibration method that consistently reduced Expected Calibration Error (ECE) and outperformed prior techniques by specifically mitigating depth-correlated over-confidence in 3D predictions.
Key insights
- Modern 3D scene understanding models, despite their high accuracy, are frequently poorly calibrated and struggle to provide reliable uncertainty estimates.
- Uncalibrated 3D models exhibit a distinct pattern of over-confidence in middle-to-far depth regions, directly correlating with 3D scene structural information.
- Factors like network capacity (larger models are less calibrated), 3D data augmentations, and specific 3D representations (fusion models exhibit better calibration) influence model reliability.
Summary by alphaXiv.
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