OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB (2410.06694v1)

Abstract: To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes.

• The paper introduces a robust framework featuring uncertainty-aware keypoint refinement that significantly enhances 6-DoF pose tracking in dynamic environments.
• It presents the OmniPose6D dataset, a large-scale synthetic collection of 40,000 sequences, designed to simulate diverse real-world trajectories for comprehensive evaluation.
• The proposed method outperforms existing benchmarks on both synthetic and real-world datasets by effectively mitigating keypoint errors and improving tracking robustness.

Overview of OmniPose6D: Short-Term Object Pose Tracking in Dynamic Scenes

The paper, "OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB," presents a framework for addressing the short-term six degrees of freedom (6-DoF) object pose tracking challenge in dynamic environments using monocular RGB inputs. This research introduces the OmniPose6D synthetic dataset, designed to replicate diverse real-world conditions. Alongside this dataset, the authors propose a benchmarking framework aimed at providing comprehensive evaluations of pose tracking algorithms.

Key Contributions

The authors highlight three primary contributions of their work:

1. Pipeline with Uncertainty-Aware Keypoint Refinement: The proposed approach integrates an uncertainty-aware keypoint refinement network, enhancing pose estimation by applying probabilistic modeling. This method surpasses existing benchmarks, especially in dynamic settings, by refining and selectively using keypoints critical to accurate pose calculation.
2. OmniPose6D Dataset: OmniPose6D is a large-scale synthetic dataset consisting of 40,000 sequences of 100 frames each. It encompasses diverse object meshes and motion trajectories, specifically curated to facilitate object pose tracking training and evaluation.
3. Benchmarks on Synthetic and Real-World Data: The proposed method demonstrates improved generalization and robustness through extensive benchmarks on both synthetic data (OmniPose6D) and real-world datasets, notably the HO3D dataset.

Methodological Insights

The paper describes a detailed approach to monocular RGB object pose tracking. It emphasizes a short-term tracking window approach using keypoint representation, which does not rely on CAD models or depth information. The method operates under the assumption that objects are rigid and that a 2D mask is available at the video’s onset.

The tracking process is initialized with a dense grid sampling technique on the mask of the first frame. Subsequent tracking uses an off-the-shelf keypoint tracker, with an additional uncertainty-aware keypoint refinement network contributing to pose estimation. This refinement process prioritizes the most reliable tracks, thereby enabling more accurate pose calculations.

Dataset Creation: OmniPose6D

OmniPose6D aims to bridge the sim-to-real gap by offering a photorealistic synthetic environment. The dataset integrates diverse trajectory modes—random walks, circling cameras, noisy trajectories, and real dataset trajectories—to emulate a comprehensive range of motion patterns. This diversity is instrumental in refining track propagation across scenes.

Evaluation and Results

The paper reports rigorous evaluation results. On the synthetic OmniPose6D and the real HO3D datasets, the authors’ approach achieves state-of-the-art results. The inclusion of an uncertainty-aware component distinguishes the model by enabling a refined selection of keypoints based on predicted reliability, thereby mitigating the impact of errors in the Structure-from-Motion framework. Quantitative results reveal significant improvements over baseline methods such as LoFTR, DROID-SLAM, TAPIR, and CoTracker.

Implications and Future Directions

The implications for such advancements span both theoretical and practical realms. Theoretically, the research contributes to the understanding of pose estimation in dynamic environments without relying on depth or model-based information. Practically, the proposed method has potential applications in areas such as robotic manipulation and augmented reality, where accurate pose tracking is essential.

Future research directions might include enhancing the framework's computational efficiency, optimizing keypoint sampling, and further advancing uncertainty estimation techniques. Additionally, exploring long-term pose tracking extensions could bridge gaps in the current short-term focus.

In conclusion, this work paves the way for more robust and adaptable object pose tracking methodologies, bolstered by an innovative dataset and the introduction of uncertainty-aware pose refinement techniques.