LiteGE: Lightweight Geodesic Embedding for Efficient Geodesics Computation and Non-Isometric Shape Correspondence

• URL: http://arxiv.org/abs/2512.17781v2
• Date: Tue, 23 Dec 2025 07:59:10 GMT
• Title: LiteGE: Lightweight Geodesic Embedding for Efficient Geodesics Computation and Non-Isometric Shape Correspondence
• Authors: Yohanes Yudhi Adikusuma, Qixing Huang, Ying He,
• Abstract summary: Recent learning-based methods achieve strong performance but rely on large 3D backbones, leading to high memory usage and latency.<n>We introduce LiteGE, a lightweight approach that constructs compact, category-aware shape descriptors.<n>Our method achieves up to 1000$times$ speedup over state-of-the-art mesh-based approaches.
• Score: 34.09193289839103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Computing geodesic distances on 3D surfaces is fundamental to many tasks in 3D vision and geometry processing, with deep connections to tasks such as shape correspondence. Recent learning-based methods achieve strong performance but rely on large 3D backbones, leading to high memory usage and latency, which limit their use in interactive or resource-constrained settings. We introduce LiteGE, a lightweight approach that constructs compact, category-aware shape descriptors by applying Principal Component Analysis (PCA) to unsigned distance field (UDFs) samples at informative voxels. This descriptor is efficient to compute and removes the need for high-capacity networks. LiteGE remains robust on sparse point clouds, supporting inputs with as few as 300 points, where prior methods fail. Extensive experiments show that LiteGE reduces memory usage and inference time by up to 300$\times$ compared to existing neural approaches. In addition, by exploiting the intrinsic relationship between geodesic distance and shape correspondence, LiteGE enables fast and accurate shape matching. Our method achieves up to 1000$\times$ speedup over state-of-the-art mesh-based approaches while maintaining comparable accuracy on non-isometric shape pairs, including evaluations on point-cloud inputs.

Related papers

• GeoPurify: A Data-Efficient Geometric Distillation Framework for Open-Vocabulary 3D Segmentation [57.8059956428009]
  Recent attempts to transfer features from 2D Vision-Language Models to 3D semantic segmentation expose a persistent trade-off.<n>We propose GeoPurify that applies a small Student Affinity Network to 2D VLM-generated 3D point features using geometric priors distilled from a 3D self-supervised teacher model.<n>Benefiting from latent geometric information and the learned affinity network, GeoPurify effectively mitigates the trade-off and achieves superior data efficiency.
  arXiv  Detail & Related papers  (2025-10-02T16:37:56Z)
• Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net [0.0]
  This paper presents an approach that combines PointNet's feature extraction with the in-memory computing capabilities and energy efficiency of neuromorphic systems fortemporal signal recognition.<n>PointNet achieves high accuracy and significantly lower energy burden during both inference and training than comparable approaches.
  arXiv  Detail & Related papers  (2024-11-21T20:48:40Z)
• A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions [42.840655419509346]
  This paper presents a novel neural framework, LoSF-UDF, for reconstructing surfaces from 3D point clouds by leveraging local shape functions.<n>Our method exhibits enhanced resilience to noise and outliers in point clouds compared to existing methods.
  arXiv  Detail & Related papers  (2024-07-01T14:39:03Z)
• PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis [28.54939134635978]
  PointeNet is a network designed specifically for point cloud analysis. Our method demonstrates flexibility by seamlessly integrating with a classification/segmentation head or embedding into off-the-shelf 3D object detection networks. Experiments on object-level datasets, including ModelNet40, ScanObjectNN, ShapeNet KITTI, and the scene-level dataset KITTI, demonstrate the superior performance of PointeNet over state-of-the-art methods in point cloud analysis.
  arXiv  Detail & Related papers  (2023-12-20T03:34:48Z)
• Learning the Geodesic Embedding with Graph Neural Networks [22.49236293942187]
  We present GeGnn, a learning-based method for computing the approximate geodesic distance between two arbitrary points on discrete polyhedra surfaces. Our key idea is to train a graph neural network to embed an input mesh into a high-dimensional embedding space. We verify the efficiency and effectiveness of our method on ShapeNet and demonstrate that our method is faster than existing methods by orders of magnitude.
  arXiv  Detail & Related papers  (2023-09-11T16:54:34Z)
• GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds [60.27217859189727]
  We propose GridPull to improve the efficiency of learning implicit representations from large scale point clouds. Our novelty lies in the fast inference of a discrete distance field defined on grids without using any neural components. We use uniform grids for a fast grid search to localize sampled queries, and organize surface points in a tree structure to speed up the calculation of distances to the surface.
  arXiv  Detail & Related papers  (2023-08-25T04:52:52Z)
• SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving [18.745798346661097]
  3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving. PointPillars, a widely adopted bird's-eye view (BEV) encoding, aggregates 3D point cloud data into 2D pillars for fast and accurate 3D object detection. We propose SPADE, an algorithm-hardware co-design strategy to maximize vector sparsity in pillar-based 3D object detection and accelerate vector-sparse convolution.
  arXiv  Detail & Related papers  (2023-05-12T14:38:49Z)
• S$^2$Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning [70.72037296392642]
  We propose a novel semi-supervised framework that allows us to learn contact from monocular images. Specifically, we leverage visual and geometric consistency constraints in large-scale datasets for generating pseudo-labels. We show benefits from using a contact map that rules hand-object interactions to produce more accurate reconstructions.
  arXiv  Detail & Related papers  (2022-08-01T14:05:23Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• Local Grid Rendering Networks for 3D Object Detection in Point Clouds [98.02655863113154]
  CNNs are powerful but it would be computationally costly to directly apply convolutions on point data after voxelizing the entire point clouds to a dense regular 3D grid. We propose a novel and principled Local Grid Rendering (LGR) operation to render the small neighborhood of a subset of input points into a low-resolution 3D grid independently. We validate LGR-Net for 3D object detection on the challenging ScanNet and SUN RGB-D datasets.
  arXiv  Detail & Related papers  (2020-07-04T13:57:43Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.