Robust 6-DoF Object Pose Tracking with Built-In Recovery under Occlusions and Rapid Object Motions

• URL: http://arxiv.org/abs/2607.23468v1
• Date: Sun, 26 Jul 2026 05:39:04 GMT
• Title: Robust 6-DoF Object Pose Tracking with Built-In Recovery under Occlusions and Rapid Object Motions
• Abstract summary: Real-time 6-DoF object pose tracking is essential for many robotics applications.<n>In this paper, we address the problem of robust model-based 6-DoF tracking of unseen objects from RGB-D data.<n>We propose a novel method that combines efficient learning-based keypoint matching with optimization-based alignment.
• Score: 15.479745132993566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time 6-DoF object pose tracking is essential for many robotics applications, and several approaches exist. Yet even today's approaches remain unreliable under temporary full occlusions and rapid object motions. Once tracking is lost, most methods struggle to detect the failure and recover automatically, often requiring manual re-initialization. In this paper, we address the problem of robust model-based 6-DoF tracking of unseen objects from RGB-D data, especially in scenarios with occlusion and fast motion. We propose a novel method that combines efficient learning-based keypoint matching with optimization-based alignment and introduces a novel failure detection and recovery module. Our system monitors pose reliability, detects tracking divergence or occlusions, and performs a global re-detection and pose estimation step that robustly verifies recovery candidates before resuming tracking. Our evaluation on standard tracking benchmarks and on a new dataset of occluded and fast-moving scenes shows that our method matches state-of-the-art accuracy on easy tracking sequences, maintains high tracking speed at 57.6 frames per second, and provides the most robust tracking performance under challenging conditions. Thus, we believe that our approach is a relevant step forward in robust 6-DoF object tracking from RGB-D data.

Related papers

• RRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes [55.15267980622371]
  RRTrack is an efficient, recoverable object 6D pose tracker.<n>It enables robust tracking through fast motion and target disappearance--reappearance.<n>Experiments show RRTrack improves equal-subset mean ADD-S AR by 66.3% and ADD-S AUC by 65.7% over FoundationPose.
  arXiv  Detail & Related papers  (2026-07-26T14:05:46Z)
• Detecting Object Tracking Failure via Sequential Hypothesis Testing [80.7891291021747]
  Real-time online object tracking in videos constitutes a core task in computer vision.<n>We propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time.<n>We propose both supervised and unsupervised variants by leveraging either ground-truth or solely internal tracking information.
  arXiv  Detail & Related papers  (2026-02-13T14:57:15Z)
• Color-Pair Guided Robust Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices [4.261261166281339]
  We present a unified framework explicitly designed for efficient execution on edge devices.<n>Key to our approach is a shared, lighting-invariant color-pair feature representation.<n>For initial estimation, this feature facilitates robust registration between the live RGB-D view and the object's 3D mesh.<n>For tracking, the same feature logic validates temporal correspondences, enabling a lightweight model to reliably regress the object's motion.
  arXiv  Detail & Related papers  (2025-09-28T05:07:49Z)
• SuperPose: Improved 6D Pose Estimation with Robust Tracking and Mask-Free Initialization [5.298176595324931]
  We developed a robust solution for real-time 6D object detection in industrial applications by integrating FoundationPose, SAM2, and LightGlue. The algorithm requires only a CAD model of the target object, with the user clicking on its location in the live feed during the initial setup. Tested on the YCB dataset and industrial components such as bleach cleanser and gears, the algorithm demonstrated reliable 6D detection and tracking.
  arXiv  Detail & Related papers  (2024-09-30T06:26:49Z)
• RTracker: Recoverable Tracking via PN Tree Structured Memory [71.05904715104411]
  We propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss.
  arXiv  Detail & Related papers  (2024-03-28T08:54:40Z)
• TrackAgent: 6D Object Tracking via Reinforcement Learning [24.621588217873395]
  We propose to simplify object tracking to a reinforced point cloud (depth only) alignment task. This allows us to train a streamlined approach from scratch with limited amounts of sparse 3D point clouds. We also show that the RL agent's uncertainty and a rendering-based mask propagation are effective reinitialization triggers.
  arXiv  Detail & Related papers  (2023-07-28T17:03:00Z)
• Learning Target Candidate Association to Keep Track of What Not to Track [100.80610986625693]
  We propose to keep track of distractor objects in order to continue tracking the target. To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision. Our tracker sets a new state-of-the-art on six benchmarks, achieving an AUC score of 67.2% on LaSOT and a +6.1% absolute gain on the OxUvA long-term dataset.
  arXiv  Detail & Related papers  (2021-03-30T17:58:02Z)
• Learning to Track with Object Permanence [61.36492084090744]
  We introduce an end-to-end trainable approach for joint object detection and tracking. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
  arXiv  Detail & Related papers  (2021-03-26T04:43:04Z)
• DEFT: Detection Embeddings for Tracking [3.326320568999945]
  We propose an efficient joint detection and tracking model named DEFT. Our approach relies on an appearance-based object matching network jointly-learned with an underlying object detection network. DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards.
  arXiv  Detail & Related papers  (2021-02-03T20:00:44Z)
• Detecting Invisible People [58.49425715635312]
  We re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects. We demonstrate that current detection and tracking systems perform dramatically worse on this task. Second, we build dynamic models that explicitly reason in 3D, making use of observations produced by state-of-the-art monocular depth estimation networks.
  arXiv  Detail & Related papers  (2020-12-15T16:54:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.