Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting

↗ arXiv ↗ Hugging Face

TL;DR

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Key Takeaways

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Why does it matter?

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ArtGS offers high-quality, efficient reconstruction of articulated objects, enhancing robotics, AR/VR, and digital twin applications. It provides scalability for complex models & sets a benchmark for future research in this field.

Visual Insights

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🔼 Table 1 presents a quantitative comparison of different methods for articulated object reconstruction on the PARIS dataset. The PARIS dataset contains both synthetic and real-world objects, each with one static and one moving part. The metrics used are commonly used for articulated object benchmarks and include measures of mesh reconstruction accuracy (Chamfer distance for the whole object (CD-w), static parts (CD-s), and moving parts (CD-m)) and articulation parameter estimation accuracy (angular error for joint axis and distance error for revolute joints). The results show the average and standard deviation over 10 trials and are given for the high-visibility joint state. The table also notes that the PARIS dataset used was augmented with depth data and that DTA was retrained for efficiency comparison. The best and second best results are highlighted to aid in readability. Finally, notes explain the meaning of F and why the Axis Pos metric is omitted for some objects.

read the caption

Table 1: Quantitative evaluation on PARIS. Metrics are reported as mean ±plus-or-minus\pm± std over 10 trials at the joint state with higher visibility, following (Weng et al., 2024). PARIS∗ (Liu et al., 2023a) is augmented with depth for fair comparison. DTA is re-trained for time efficiency comparison. Lower (↓↓\downarrow↓) is better on all metrics and we highlight best and second best results. Objects with ††\dagger† are seen categories trained in Ditto. F indicates wrong motion type predictions. Axis Pos. is omitted for prismatic joints (Blade, Storage, and Real Storage).

In-depth insights

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ArtGS: Overview

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ArtGS appears to be a novel approach to reconstructing articulated objects from multi-view images, leveraging 3D Gaussians for representation. The core idea seems to revolve around efficiently integrating information across different object states to improve reconstruction accuracy. The method likely involves canonical Gaussians with a coarse-to-fine initialization strategy, and a skinning-inspired part dynamics modeling module to enhance both mesh reconstruction and articulation learning. The emphasis seems to be on handling complex multi-part articulated objects, addressing limitations of existing methods that struggle with integrating information across different object states, and part dynamics modeling, particularly for multi-part objects, indicating a significant advancement in the field.

Gaussians Refined

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Refining Gaussians in this context likely involves iteratively improving the parameters of the 3D Gaussians used to represent the articulated object. This could encompass adjusting their position, covariance (shape), opacity, and color to better fit the observed multi-view images and enforce articulation constraints. The refinement process probably uses a loss function that combines photometric consistency (matching rendered images to input views) with geometric regularization and articulation priors. Key is to leverage motion cues between states for guiding the refining, making them canonical. The optimization strategy is crucial, balancing exploration (allowing Gaussians to move and change significantly) with exploitation (fine-tuning existing Gaussians). Coarse-to-fine is the key in initialization and skinning-inspired clustering for refinement.

Part Discovery

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Part discovery in articulated object reconstruction is crucial for understanding object functionality and enabling realistic interactions. Accurately identifying individual parts is challenging due to occlusions, varying appearances, and complex kinematic relationships. Successful part discovery enables finer-grained control over object manipulation and a deeper understanding of object dynamics, improving sim-to-real transfer in robotics. Unsupervised techniques leverage geometric cues and motion patterns to segment objects, while supervised methods rely on pre-existing part annotations or learned priors. The ability to discover object parts is tightly coupled to understanding articulation parameters; errors in one domain may lead to errors in another. Effectively addressing this requires joint optimization strategies, while further work needs more emphasis on how part discovery can benefit interactive tasks.

State Alignment

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State alignment is crucial for articulated object reconstruction, especially when dealing with limited views. Existing methods often struggle to effectively integrate information across different object states, leading to inaccuracies in part-mesh reconstruction and dynamics modeling. A key challenge is to simultaneously learn and align fine-grained object parts across different states, which is essential for accurately reconstructing complex articulated objects. Techniques that can effectively bridge the gap between different object states, such as using canonical Gaussians, are essential for enabling accurate deformation modeling and improving both mesh reconstruction and articulation learning. Proper initialization and update schemes for these representations can further enhance the accuracy and efficiency of state alignment, leading to more robust and reliable reconstruction results. This careful alignment process is key to building interactable digital replicas.

Limited States

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The paper acknowledges that the current approach, ArtGS, is limited to modeling articulated objects using only two states, which might not fully represent real-world complexity, especially multi-part objects. As part count increases, distinguishing parts with similar joint axes and motion patterns becomes a challenge, complicating segmentation. Potential future research includes extending ArtGS to handle multiple states sequentially, and adapting ArtGS for reconstruction from monocular video, leveraging temporal information to infer a continuous range of articulation states. It allows better understanding of the object movement.

More visual insights

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Full paper

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