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DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios
Computer Vision and Pattern Recognition · Submitted 25 Mar 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim To validate the effectiveness and value of our dataset, we perform comprehensive evaluations using 18 state-of-the-art methods, demonstrating its potential to accelerate research in this challenging domain.
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The abstract, sentence by sentence
In the realm of object pose estimation, scenarios involving both dynamic objects and moving cameras are prevalent. However, the scarcity of corresponding real-world datasets significantly hinders the development and evaluation of robust pose estimation models. This is largely attributed to the inherent challenges in accurately annotating object poses in dynamic scenes captured by moving cameras. To bridge this gap, this paper presents a novel dataset DynOPETs and a dedicated data acquisition and annotation pipeline tailored for object pose estimation and tracking in such unconstrained environments. Our efficient annotation method innovatively integrates pose estimation and pose tracking techniques to generate pseudo-labels, which are subsequently refined through pose graph optimization. The resulting dataset offers accurate pose annotations for dynamic objects observed from moving cameras. To validate the effectiveness and value of our dataset, we perform comprehensive evaluations using 18 state-of-the-art methods, demonstrating its potential to accelerate research in this challenging domain. The dataset will be made publicly available to facilitate further exploration and advancement in the field.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In the realm of object pose estimation, scenarios involving both dynamic objects and moving cameras are prevalent. | background 1.00 | 0.07 | 0.02 | asserted 1.00 |
| s1 | However, the scarcity of corresponding real-world datasets significantly hinders the development and evaluation of robust pose estimation models. | gap 0.94 | 0.10 | 0.03 | asserted 1.00 |
| s2 | This is largely attributed to the inherent challenges in accurately annotating object poses in dynamic scenes captured by moving cameras. | background 0.60 | 0.08 | 0.02 | asserted 0.92 |
| s3 | To bridge this gap, this paper presents a novel dataset DynOPETs and a dedicated data acquisition and annotation pipeline tailored for object pose estimation and tracking in such unconstrained environments. | contribution 1.00 | 0.93 | 0.18 | asserted 1.00 |
| s4 | Our efficient annotation method innovatively integrates pose estimation and pose tracking techniques to generate pseudo-labels, which are subsequently refined through pose graph optimization. | method 0.99 | 0.95 | 0.06 | asserted 1.00 |
| s5 | The resulting dataset offers accurate pose annotations for dynamic objects observed from moving cameras. | result 0.68 | 0.92 | 0.17 | asserted 1.00 |
| s6 | To validate the effectiveness and value of our dataset, we perform comprehensive evaluations using 18 state-of-the-art methods, demonstrating its potential to accelerate research in this challenging domain. | result 0.66 | 0.97 | 0.41 | asserted 0.94 |
| s7 | The dataset will be made publicly available to facilitate further exploration and advancement in the field. | future work 0.67 | 0.42 | 0.09 | asserted 0.97 |
What was examined, and what was not
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The paper
Original abstract
In the realm of object pose estimation, scenarios involving both dynamic objects and moving cameras are prevalent. However, the scarcity of corresponding real-world datasets significantly hinders the development and evaluation of robust pose estimation models. This is largely attributed to the inherent challenges in accurately annotating object poses in dynamic scenes captured by moving cameras. To bridge this gap, this paper presents a novel dataset DynOPETs and a dedicated data acquisition and annotation pipeline tailored for object pose estimation and tracking in such unconstrained environments. Our efficient annotation method innovatively integrates pose estimation and pose tracking techniques to generate pseudo-labels, which are subsequently refined through pose graph optimization. The resulting dataset offers accurate pose annotations for dynamic objects observed from moving cameras. To validate the effectiveness and value of our dataset, we perform comprehensive evaluations using 18 state-of-the-art methods, demonstrating its potential to accelerate research in this challenging domain. The dataset will be made publicly available to facilitate further exploration and advancement in the field.
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How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 4 sentences without a confident label
Uses the method or tool of 2 papers
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Our method adopts a similar strategy but replaces the keypoint detection and matching component with a state-of-the-art Track Any Point (TAP) model [ref] .
CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos · Relative Object Pose Estimation via Point Tracking · 0.99
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To ensure fairness, each sequence is paired with a specific CAD model and uniform segmentations produced by SAM2 [ref] .
SAM 2: Segment Anything in Images and Videos · Unseen Object Pose Estimation · 0.98
Cites as background 2 papers
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Furthermore, some methods directly predict object pose, employing either enhanced geometric feature extractors [ref] or a combination of geometric and semantic priors [ref] .
GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation · Category-level Object Pose Estimation · 0.97
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Unlike single-frame object pose estimation methods, object pose tracking leverage temporal information across video sequences to estimate object poses [ref] .
2405.07801 · Object Pose Tracking · 0.95
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