# Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects arXiv: https://arxiv.org/abs/2203.05334 ยท HTML: https://mlsanity.com/paper/2203.05334/ ## Key facts - Cited by 30 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 80 references that are in the map. - First posted on arXiv 10 Mar 2022. ## Builds on (most cited first, 20 of 20) - [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016) - [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017) - [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://mlsanity.com/paper/1901.04780/) (2019) - [CosyPose: Consistent multi-view multi-object 6D pose estimation](https://mlsanity.com/paper/2008.08465/) (2020) - [DeepIM: Deep Iterative Matching for 6D Pose Estimation](https://mlsanity.com/paper/1804.00175/) (2018) - [PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1911.04231/) (2019) - [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020) - [Implicit 3D Orientation Learning for 6D Object Detection from RGB Images](https://mlsanity.com/paper/1902.01275/) (2019) - [FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation](https://mlsanity.com/paper/2103.02242/) (2021) - [PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking](https://mlsanity.com/paper/1905.09304/) (2019) - [se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains](https://mlsanity.com/paper/2007.13866/) (2020) - [6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints](https://mlsanity.com/paper/1910.10750/) (2019) - [Deep Model-Based 6D Pose Refinement in RGB](https://mlsanity.com/paper/1810.03065/) (2018) - [Depth-Based Object Tracking Using a Robust Gaussian Filter](https://mlsanity.com/paper/1602.06157/) (2016) - [Probabilistic Object Tracking using a Range Camera](https://mlsanity.com/paper/1505.00241/) (2015) - [A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking](https://mlsanity.com/paper/1807.02087/) (2018) - [SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World](https://mlsanity.com/paper/2110.12715/) (2021) - [Probabilistic Articulated Real-Time Tracking for Robot Manipulation](https://mlsanity.com/paper/1610.04871/) (2016) - [Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core](https://mlsanity.com/paper/1911.10249/) (2019) - [Combining 3D Model Contour Energy and Keypoints for Object Tracking](https://mlsanity.com/paper/2002.01379/) (2020) ## Built on by (most cited first, 30 of 30) - [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023) - [BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects](https://mlsanity.com/paper/2303.14158/) (2023) - [Visibility Aware Human-Object Interaction Tracking from Single RGB Camera](https://mlsanity.com/paper/2303.16479/) (2023) - [One2Any: One-Reference 6D Pose Estimation for Any Object](https://mlsanity.com/paper/2505.04109/) (2025) - [PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments](https://mlsanity.com/paper/2312.15130/) (2023) - [Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors](https://mlsanity.com/paper/2301.13667/) (2023) - [6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics](https://mlsanity.com/paper/2303.13241/) (2023) - [GoTrack: Generic 6DoF Object Pose Refinement and Tracking](https://mlsanity.com/paper/2506.07155/) (2025) - [Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking](https://mlsanity.com/paper/2302.11458/) (2023) - [GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance](https://mlsanity.com/paper/2402.07677/) (2024) - [UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching](https://mlsanity.com/paper/2409.01782/) (2024) - [ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation](https://mlsanity.com/paper/2403.16400/) (2024) - [PRISM-DP: Spatial Pose-based Observations for Diffusion-Policies via Segmentation, Mesh Generation, and Pose Tracking](https://mlsanity.com/paper/2504.20359/) (2025) - [Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2508.04122/) (2025) - [A Multi-body Tracking Framework - From Rigid Objects to Kinematic Structures](https://mlsanity.com/paper/2208.01502/) (2022) - [For A More Comprehensive Evaluation of 6DoF Object Pose Tracking](https://mlsanity.com/paper/2309.07796/) (2023) - [Toward a Plug-and-Play Vision-Based Grasping Module for Robotics](https://mlsanity.com/paper/2310.04349/) (2023) - [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) - [YCB-Ev 1.1: Event-vision dataset for 6DoF object pose estimation](https://mlsanity.com/paper/2309.08482/) (2023) - [ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization](https://mlsanity.com/paper/2401.08937/) (2024) - [Robust 6DoF Pose Tracking Considering Contour and Interior Correspondence Uncertainty for AR Assembly Guidance](https://mlsanity.com/paper/2502.11971/) (2025) - [CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation](https://mlsanity.com/paper/2511.12919/) (2025) - [MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objects](https://mlsanity.com/paper/2603.22839/) (2026) - [Counter-Hypothetical Particle Filters for Single Object Pose Tracking](https://mlsanity.com/paper/2305.17828/) (2023) - [Active Control Points-based 6DoF Pose Tracking for Industrial Metal Objects](https://mlsanity.com/paper/2507.01478/) (2025) - [DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects](https://mlsanity.com/paper/2510.10933/) (2025) - [Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers](https://mlsanity.com/paper/2604.12509/) (2026) - [Robust 6-DoF Object Pose Tracking with Built-In Recovery under Occlusions and Rapid Object Motions](https://mlsanity.com/paper/2607.23468/) (2026) - [RRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes](https://mlsanity.com/paper/2607.23669/) (2026) ## Abstract (arXiv) Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision. State-of-the-art approaches often rely on object texture to tackle this problem. However, while they achieve impressive results, many objects do not contain sufficient texture, violating the main underlying assumption. In the following, we thus propose ICG, a novel probabilistic tracker that fuses region and depth information and only requires the object geometry. Our method deploys correspondence lines and points to iteratively refine the pose. We also implement robust occlusion handling to improve performance in real-world settings. Experiments on the YCB-Video, OPT, and Choi datasets demonstrate that, even for textured objects, our approach outperforms the current state of the art with respect to accuracy and robustness. At the same time, ICG shows fast convergence and outstanding efficiency, requiring only 1.3 ms per frame on a single CPU core. Finally, we analyze the influence of individual components and discuss our performance compared to deep learning-based methods. The source code of our tracker is publicly available.