Uncertainty Estimation for 3D
Object Detection via Evidential Learning

Nikita Durasov1, Rafid Mahmood2, Jiwoong Choi2, Marc Law2, James Lucas2, Pascal Fua1, Jose Alvarez 2,
1Computer Vision Laboratory, EPFL 2NVIDIA
arXiv 2024
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Teaser Image

3D Object Detection Uncertainty Estimation Framework. Our Evidential Deep Learning approach jointly generates heatmap probabilities for objects within Bird's Eye View and their corresponding uncertainty values, which allows us to detect several critical problems within autonomous driving, namely (left) identifying out-of-distribution scenes (e.g., with bad weather conditions), (middle) erroneous predicted boxes, and (right) missed objects (e.g., missed grey and white cars in the image). The uncertainty estimates guide selective human verification, leading to improvements in detection metrics (e.g., mean Average Precision (mAP) and nuScenes Detection Score (NDS)).

Abstract

3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics. However, models often struggle to quantify detection reliability, leading to poor performance on unfamiliar scenes. We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. These uncertainty estimates require minimal computational overhead and are generalizable across different architectures. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections; our framework consistently improves over baselines by 10-20% on average. Finally, we integrate this suite of tasks into a system where a 3D object detector auto-labels driving scenes and our uncertainty estimates verify label correctness before the labels are used to train a second model. Here, our uncertainty-driven verification results in a 1% improvement in mAP and a 1-2% improvement in NDS.

BibTeX

@article{durasov2024uncertainty,
  title = {Uncertainty Estimation for 3D Object Detection via Evidential Learning},
  author = {Durasov, Nikita and Mahmood, Rafid and Choi, Jiwoong and Law, Marc T and Lucas, James and Fua, Pascal and Alvarez, Jose M}, 
  journal = {arXiv preprint arXiv:2410.23910},
  year = {2024}
}