# Probabilistic Object Tracking using a Range Camera

arXiv: https://arxiv.org/abs/1505.00241 · HTML: https://mlsanity.com/paper/1505.00241/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2015.

## Built on by (most cited first, 21 of 21)

- [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)
- [CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds](https://mlsanity.com/paper/2104.03437/) (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)
- [Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains](https://mlsanity.com/paper/2105.14391/) (2021)
- [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)
- [Event6D: Event-based Novel Object 6D Pose Tracking](https://mlsanity.com/paper/2603.28045/) (2026)
- [Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping](https://mlsanity.com/paper/2103.05401/) (2021)
- [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)
- [Physics-Based Object 6D-Pose Estimation during Non-Prehensile Manipulation](https://mlsanity.com/paper/2211.13572/) (2022)
- [Counter-Hypothetical Particle Filters for Single Object Pose Tracking](https://mlsanity.com/paper/2305.17828/) (2023)
- [OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB](https://mlsanity.com/paper/2410.06694/) (2024)

## Abstract (arXiv)

We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.