TACO-Net: Topological Signatures Triumph in 3D Object Classification

2025-09-29

AI Summary

To address the challenges of low 3D object classification accuracy caused by point cloud disorder, irregularity, and noise sensitivity, this paper proposes a robust classification method integrating topological data analysis (TDA) with multi-scale image filtering. The method voxelizes raw point clouds into binary images, extracts topological features—particularly persistent homology—and employs a lightweight 1D CNN for end-to-end feature learning and classification. Crucially, it circumvents the strong reliance of conventional point cloud networks on local geometric modeling, thereby enhancing noise robustness and generalization. Evaluated on ModelNet40 and ModelNet10, the method achieves state-of-the-art classification accuracies of 99.05% and 99.52%, respectively. Moreover, it demonstrates superior robustness under diverse synthetic noise patterns and on the real-world OmniObject3D dataset, validating its practical efficacy and scalability.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: 3D Computer Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

3D object classification is a crucial problem due to its significant practical relevance in many fields, including computer vision, robotics, and autonomous driving. Although deep learning methods applied to point clouds sampled on CAD models of the objects and/or captured by LiDAR or RGBD cameras have achieved remarkable success in recent years, achieving high classification accuracy remains a challenging problem due to the unordered point clouds and their irregularity and noise. To this end, we propose a novel state-of-the-art (SOTA) 3D object classification technique that combines topological data analysis with various image filtration techniques to classify objects when they are represented using point clouds. We transform every point cloud into a voxelized binary 3D image to extract distinguishing topological features. Next, we train a lightweight one-dimensional Convolutional Neural Network (1D CNN) using the extracted feature set from the training dataset. Our framework, TACO-Net, sets a new state-of-the-art by achieving $99.05%$ and $99.52%$ accuracy on the widely used synthetic benchmarks ModelNet40 and ModelNet10, and further demonstrates its robustness on the large-scale real-world OmniObject3D dataset. When tested with ten different kinds of corrupted ModelNet40 inputs, the proposed TACO-Net demonstrates strong resiliency overall.

Problem

• Classifying 3D objects from unordered, noisy point clouds
• Improving classification accuracy for LiDAR and RGBD data
• Enhancing robustness against corrupted inputs in 3D recognition

Innovation

• Combines topological data analysis with image filtration
• Transforms point clouds into voxelized binary 3D images
• Trains lightweight 1D CNN on extracted topological features