# Depth-Based Object Tracking Using a Robust Gaussian Filter

arXiv: https://arxiv.org/abs/1602.06157 · HTML: https://mlsanity.com/paper/1602.06157/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2016.

## Built on by (most cited first, 22 of 22)

- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models](https://mlsanity.com/paper/2108.00516/) (2021)
- [Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects](https://mlsanity.com/paper/2203.05334/) (2022)
- [Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation](https://mlsanity.com/paper/2205.11047/) (2022)
- [Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks](https://mlsanity.com/paper/2106.14070/) (2021)
- [One2Any: One-Reference 6D Pose Estimation for Any Object](https://mlsanity.com/paper/2505.04109/) (2025)
- [Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild](https://mlsanity.com/paper/2209.12009/) (2022)
- [GoTrack: Generic 6DoF Object Pose Refinement and Tracking](https://mlsanity.com/paper/2506.07155/) (2025)
- [GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2203.02119/) (2022)
- [GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance](https://mlsanity.com/paper/2402.07677/) (2024)
- [Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains](https://mlsanity.com/paper/2105.14391/) (2021)
- [PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Tracking](https://mlsanity.com/paper/2504.20359/) (2025)
- [Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker](https://mlsanity.com/paper/2401.04377/) (2024)
- [6-DoF Object Tracking with Event-based Optical Flow and Frames](https://mlsanity.com/paper/2508.14776/) (2025)
- [A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures](https://mlsanity.com/paper/2107.01829/) (2021)
- [A Multi-body Tracking Framework - From Rigid Objects to Kinematic Structures](https://mlsanity.com/paper/2208.01502/) (2022)
- [Event6D: Event-based Novel Object 6D Pose Tracking](https://mlsanity.com/paper/2603.28045/) (2026)
- [TrackAgent: 6D Object Tracking via Reinforcement Learning](https://mlsanity.com/paper/2307.15671/) (2023)
- [CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation](https://mlsanity.com/paper/2511.12919/) (2025)
- [Event-based Motion & Appearance Fusion for 6D Object Pose Tracking](https://mlsanity.com/paper/2603.08264/) (2026)
- [OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB](https://mlsanity.com/paper/2410.06694/) (2024)
- [Temporally Consistent Object 6D Pose Estimation for Robot Control](https://mlsanity.com/paper/2605.02708/) (2026)

## Abstract (arXiv)

We consider the problem of model-based 3D-tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this problem. First of all, depth sensors are characterized by fat-tailed measurement noise. To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand. Thereby, we avoid using heuristic outlier detection methods that simply reject measurements if they do not match the model. Secondly, the computational cost of the standard Gaussian filter is prohibitive due to the high-dimensional measurement, i.e. the depth image. To address this problem, we propose an approximation to reduce the computational complexity of the filter. In quantitative experiments on real data we show how our method clearly outperforms the standard Gaussian filter. Furthermore, we compare its performance to a particle-filter-based tracking method, and observe comparable computational efficiency and improved accuracy and smoothness of the estimates.