# Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss

arXiv: https://arxiv.org/abs/2011.02553 · HTML: https://mlsanity.com/paper/2011.02553/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge](https://mlsanity.com/paper/2309.09593/) (2023)
- [Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction](https://mlsanity.com/paper/2210.07424/) (2022)
- [LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2206.00214/) (2022)
- [Uncertainty-Aware AB3DMOT by Variational 3D Object Detection](https://mlsanity.com/paper/2302.05923/) (2023)
- [Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift](https://mlsanity.com/paper/2605.05328/) (2026)
- [Variational Voxel Pseudo Image Tracking](https://mlsanity.com/paper/2302.05914/) (2023)
- [Variational Graph Convolutional Neural Networks](https://mlsanity.com/paper/2507.01699/) (2025)
- [Communication-Efficient Multi-Agent 3D Detection via Hybrid Collaboration](https://mlsanity.com/paper/2508.07092/) (2025)
- [Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment](https://mlsanity.com/paper/2605.16087/) (2026)

## Abstract (arXiv)

Object detection and tracking is a key task in autonomy. Specifically, 3D object detection and tracking have been an emerging hot topic recently. Although various methods have been proposed for object detection, uncertainty in the 3D detection and tracking tasks has been less explored. Uncertainty helps us tackle the error in the perception system and improve robustness. In this paper, we propose a method for improving target tracking performance by adding uncertainty regression to the SECOND detector, which is one of the most representative algorithms of 3D object detection. Our method estimates positional and dimensional uncertainties with Gaussian Negative Log-Likelihood (NLL) Loss for estimation and introduces von-Mises NLL Loss for angular uncertainty estimation. We fed the uncertainty output into a classical object tracking framework and proved that our method increased the tracking performance compared against the vanilla tracker with constant covariance assumption.