Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation (2306.05584v2)

Abstract: A truly generalizable approach to rigid segmentation and motion estimation is fundamental to 3D understanding of articulated objects and moving scenes. In view of the closely intertwined relationship between segmentation and motion estimates, we present an SE(3) equivariant architecture and a training strategy to tackle this task in an unsupervised manner. Our architecture is composed of two interconnected, lightweight heads. These heads predict segmentation masks using point-level invariant features and estimate motion from SE(3) equivariant features, all without the need for category information. Our training strategy is unified and can be implemented online, which jointly optimizes the predicted segmentation and motion by leveraging the interrelationships among scene flow, segmentation mask, and rigid transformations. We conduct experiments on four datasets to demonstrate the superiority of our method. The results show that our method excels in both model performance and computational efficiency, with only 0.25M parameters and 0.92G FLOPs. To the best of our knowledge, this is the first work designed for category-agnostic part-level SE(3) equivariance in dynamic point clouds.

• The paper presents a novel unsupervised SE(3) equivariant architecture that jointly tackles multi-body rigid segmentation and motion estimation.
• It features two lightweight network heads that leverage SE(3)-invariant and equivariant features for accurate segmentation and motion estimation, respectively.
• Empirical results across multiple datasets show enhanced segmentation accuracy and motion fidelity with only 0.25M parameters and 0.92G FLOPs.

An Overview of Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation

Understanding and modeling 3D scenes involving articulated objects and dynamic environments require effective segmentation and estimation of motion, particularly within the rigid multi-body framework. This paper presents a novel SE(3) equivariant architecture, utilizing group equivariance to tackle these challenges unsupervisedly. This essay provides an expert overview of the architecture, its training methodology, and the resulting empirical performance.

SE(3) Equivariant Architecture

The proposed model operates on the assumption that 3D motion can be characterized through combined rigid movements within multi-body systems. It comprises two lightweight, interconnected network heads: one for segmentation tasks and the other for motion estimation.

1. Segmentation Head: The segmentation head outputs point-wise segmentation masks using SE(3)-invariant features. Unlike traditional models which leverage object category-specific information, this architecture is designed to be category-agnostic, enhancing its adaptability and generalization to varying 3D structures.
2. Motion Estimation Head: This head computes motion estimates from SE(3) equivariant features. The SE(3) equivariance ensures that the features adapt coherently to the rigid transformations, enhancing the model's robustness to transformations, particularly in systems experiencing unseen motion variations.

The integration of these two components results in a highly efficient and computationally lightweight model, characterized by only 0.25M parameters and 0.92G FLOPs, enabling its broad applicability and rapid deployment across diverse scenarios.

Unified Training Strategy

The unsupervised training methodology leverages the intertwined nature of segmentation and motion estimation. Importantly, the method employs scene flow as an auxiliary mechanism to interrelate scene transformations, leverages segmentation masks, and compensates for possible estimation errors.

• Scene Flow Utilization: A key innovation lies in using scene flow to bridge the estimates of segmentation masks and motion. By establishing a feedback loop, scene flow is incrementally refined, subsequently refining segmentation and motion estimates.
• Optimization without Manual Intervention: The paper introduces a seamless online optimization process free from manually intensive methods, such as Markov Chain Monte Carlo techniques often used in existing literature, thereby reducing complexity and potential error propagation.

Empirical Evaluation

The architecture demonstrates strong empirical performance across several benchmarks — tested on four datasets including SAPIEN, KITTI-SF, OGC-DR, and OGC-DRSV, spanning articulated objects and vehicular scenes.

• On the SAPIEN dataset, it notably achieves a substantial gain in segmentation accuracy, cross-evaluated against existing state-of-the-art methods. It consistently surpasses benchmarks in metrics such as average precision (AP) and efficiency in computational parameters.
• The model also proves adept at motion estimation, where predictions approach those achieved by fully supervised counterparts. The EPE3D metrics convey that the method captures motion dynamics with high fidelity.

Implications and Future Directions

The research presented promotes advancements in autonomous understanding of complex 3D environments without explicit supervision, potentially facilitating its applications in autonomous driving, robotics, and virtual reality. Future advancements may consider the integration of partially deformable systems to further generalize the model's applicability and resilience, particularly within environments that involve flexible bodies or non-rigid scene elements. Additionally, extending the current methodologies may involve exploring hybrid models that can balance between supervised and unsupervised learning paradigms to refine the understanding and prediction accuracy of intricate 3D dynamics.