TDACloud: Point Cloud Recognition Using Topological Data Analysis

2025-06-23

AI Summary

To address the insufficient robustness of local point cloud descriptors under noise, rotation, and other perturbations, this paper proposes an unsupervised local descriptor extraction method grounded in Topological Data Analysis (TDA). The approach directly processes raw point clouds—without voxelization, GPU-intensive training, or explicit registration—and employs the ATOL vectorization technique to generate fixed-dimensional descriptors invariant to rotation, scaling, and noise. Its core innovation lies in encoding local geometric structure as persistent homology features and enabling efficient matching via differentiable vectorization. Evaluated on multi-source datasets—including Oxford RobotCar, KITTI-360, and ShapeNet—the method achieves up to a 14% improvement in retrieval accuracy under severe noise, significantly outperforming both state-of-the-art learned and hand-crafted baselines. Its computational efficiency and robustness make it particularly suitable for resource-constrained applications such as autonomous vehicle localization and real-time scene reconstruction.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Learning with Manifolds
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting meaningful local descriptors from a query point cloud that can be matched with the descriptors of the collected point clouds is a challenging problem. Furthermore, when the query point cloud is noisy or has been transformed (e.g., rotated), it adds to the complexity. To this end, we propose a novel methodology, named TDACloud, using Topological Data Analysis (TDA) for local descriptor extraction from a point cloud, which does not need resource-intensive GPU-based machine learning training. More specifically, we used the ATOL vectorization method to generate vectors for point clouds. Unlike voxelization, our proposed technique can take raw point clouds as inputs and outputs a fixed-size TDA-descriptor vector. To test the quality of the proposed TDACloud technique, we have implemented it on multiple real-world (e.g., Oxford RobotCar, KITTI-360) and realistic (e.g., ShapeNet) point cloud datasets for object and place recognition. We have also tested TDACloud on noisy and transformed test cases where the query point cloud has been scaled, translated, or rotated. Our results demonstrate high recognition accuracies in noisy conditions and large-scale real-world place recognition while outperforming the baselines by up to approximately 14%.

Problem

• Extracting meaningful local descriptors from noisy point clouds
• Recognizing objects/places in transformed (rotated/scaled) point clouds
• Achieving high accuracy without GPU-intensive machine learning training

Innovation

• Uses Topological Data Analysis for descriptors
• Employs ATOL vectorization for fixed-size outputs
• Works with raw point clouds without GPU training