Gaussian Processes for 3D Covariance Estimation

Precise covariance estimation is critical in intelligent vehicles and autonomous systems, as many algorithms rely on it to optimize performance. However, covariance values are often arbitrarily defined due to a lack of reliable information. Many systems provide scores that do not directly reflect positional or dimensional uncertainty, limiting the interpretability and reliability of these systems in real-world applications. This paper proposes a novel Gaussian Process-based algorithm to enhance 3D detection outputs by estimating the covariance of inferred attributes. Unlike previous methods, the proposed approach directly quantifies uncertainty using detections in a sensor-agnostic manner, offering robust and interpretable covariance estimates. The method integrates statistical tools, including interval segmentation strategies and variance analysis, to refine error estimation across different detection classes and spatial dimensions. The proposed framework is evaluated using a KITTI-based dataset under diverse scenarios, demonstrating that ground-truth detections consistently fall within the inferred uncertainty regions. The experimental results highlight the method's high performance in uncertainty estimation, leading to more reliable sensor fusion, object tracking, and localization in intelligent systems.