Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds (2402.10865v1)

Abstract: We investigate a variation of the 3D registration problem, named multi-model 3D registration. In the multi-model registration problem, we are given two point clouds picturing a set of objects at different poses (and possibly including points belonging to the background) and we want to simultaneously reconstruct how all objects moved between the two point clouds. This setup generalizes standard 3D registration where one wants to reconstruct a single pose, e.g., the motion of the sensor picturing a static scene. Moreover, it provides a mathematically grounded formulation for relevant robotics applications, e.g., where a depth sensor onboard a robot perceives a dynamic scene and has the goal of estimating its own motion (from the static portion of the scene) while simultaneously recovering the motion of all dynamic objects. We assume a correspondence-based setup where we have putative matches between the two point clouds and consider the practical case where these correspondences are plagued with outliers. We then propose a simple approach based on Expectation-Maximization (EM) and establish theoretical conditions under which the EM approach converges to the ground truth. We evaluate the approach in simulated and real datasets ranging from table-top scenes to self-driving scenarios and demonstrate its effectiveness when combined with state-of-the-art scene flow methods to establish dense correspondences.

• The paper introduces an EM-based method that recovers movements of multiple objects without needing prior knowledge of their count.
• The methodology leverages iterative clustering and expectation-maximization to refine object poses amid noise and clutter.
• Empirical evaluations show superior accuracy in dynamic, real-world scenarios compared to conventional registration techniques.

Multi-Model 3D Registration Through Expectation-Maximization

Introduction to Multi-Model 3D Registration

The problem of 3D registration is pivotal in the fields of robotics and computer vision, playing a crucial role in applications such as motion estimation, object pose estimation, and medical imaging. Traditionally, 3D registration problems have focused on identifying the rotation and translation between two point clouds to reconstruct a single pose. This research, however, ventures into the more complex territory of multi-model 3D registration. Here, the objective is to discern the movement of multiple objects between two point clouds that may also include points from the background. This variant not only generalizes the standard 3D registration problem but also aligns with practical scenarios, such as estimating the motion of dynamic objects perceived by a robot's depth sensor in a cluttered environment.

Robust 3D Registration

The paper introduces a robust approach to multi-model 3D registration, acknowledging that real-world applications involve measurements contaminated with outliers. To address this, the authors propose an Expectation-Maximization (EM) based method capable of handling putative matches between point clouds that contain significant outliers. The approach iteratively computes the assignments of measurements to objects, establishing a practical framework for reconstructing the pose of each object without the need for prior knowledge of the number of objects present.

Expectation-Maximization Approach

The introduced EM algorithm iteratively refines the assignments of points to objects and the pose of each object. This process begins with an initial guess of object clusters and alternates between expectation and maximization steps to update cluster assignments and compute the pose and variance for each cluster. A critical insight provided is that good initial clustering, achievable through simple Euclidean clustering or advanced techniques like SegmentAnything (SAM), is pivotal for the convergence of the EM algorithm towards the ground truth clusters.

Theoretical Analysis and Practical Implications

A novel theoretical analysis assures that the EM scheme, under specific conditions related to the initial clustering, can indeed recover the true motion of all objects of interest. This promise of convergence towards ground truth underlines the method's effectiveness and lays down a theoretical foundation for further exploration.

Empirical Evaluation

The approach is rigorously evaluated against state-of-the-art methods across various datasets ranging from synthetic table-top scenes to real-life self-driving scenarios. The results demonstrate notable effectiveness, particularly in scenarios complicated by noise or identical motion patterns among different objects. The method shows superior performance in terms of accuracy in object pose estimation and object clustering as compared to traditional methods like Sequential RANSAC and T-Linkage.

Future Directions in AI and Robotics

This work opens up several avenues for future research. The successful application of the EM algorithm in multi-model 3D registration could inspire similar approaches in other complex registration problems. Moreover, the integration of learning-based methods for initial clustering or the use of more sophisticated models for handling outlier correspondences presents potential areas for enhancement. This research not only marks a significant step forward in the domain of 3D registration but also sets a robust foundation for tackling dynamic scene understanding and reconstruction in robotics and computer vision.

In summary, this work addresses the critical challenge of multi-model 3D registration with an EM-based approach, fortified by rigorous theoretical analysis and compelling empirical evidence. The implications for practical robotics applications are profound, paving the way for more accurate and robust methods of understanding and interacting with dynamic environments.