TopoRec: Point Cloud Recognition Using Topological Data Analysis
Anirban Ghosh, Iliya Kulbaka, Ian Dahlin, Ayan Dutta

TL;DR
TopoRec leverages Topological Data Analysis to extract robust global descriptors from point clouds, enabling accurate, training-free recognition in various environments, outperforming existing methods.
Contribution
The paper introduces TopoRec, a novel TDA-based approach for point cloud recognition that eliminates the need for training and generalizes well across datasets.
Findings
Outperforms state-of-the-art learning-based methods in accuracy.
Does not require extensive training, enabling easy adaptation.
Demonstrates strong generalization across diverse datasets.
Abstract
Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting a meaningful global descriptor from a query point cloud that can be matched with the descriptors of the database 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 TopoRec, which utilizes Topological Data Analysis (TDA) for extracting local descriptors from a point cloud, thereby eliminating the need for resource-intensive GPU-based machine learning training. More specifically, we used the ATOL vectorization method to generate vectors for point clouds. To test the quality of the proposed TopoRec technique, we have implemented it on multiple real-world (e.g., Oxford…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopological and Geometric Data Analysis · 3D Shape Modeling and Analysis · Morphological variations and asymmetry
