mlsanityData to 7 Oct 2026

A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation

arXiv 2402.12797 ↗First posted 20 Feb 2024cs.CV cs.CGcited by 1

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Abstract (arXiv)

We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a whole, eliminating the need for splitting input structure into multiple segments. We represent the tubular shape as a truncated signed distance function (TSDF) in a voxel hashing manner, in which the signed distance between a voxel center and the object is computed through a simple geometric algorithm. Our method does not involve any surface sampling scheme or solving large matrix equations, and therefore is a faster and more elegant solution for tubular shape reconstruction compared to other approaches. Experiments demonstrate the efficiency and effectiveness of the proposed method. Code is avaliable at https://github.com/wlsdzyzl/Dragon.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

24 of 49 references.

References · 24

  1. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  2. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  3. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  4. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  5. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
  6. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  7. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  8. 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 286
  9. 2016Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesiscited by 219
  10. 2019Topology-Preserving Deep Image Segmentationcited by 198
  11. 2022NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fieldscited by 188
  12. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
12 more
  1. 2017SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsiscited by 128
  2. 2020Point Cloud Completion by Skip-attention Network with Hierarchical Foldingcited by 106
  3. 2018DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumescited by 62
  4. 2022ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Imagescited by 54
  5. 2019Deep Distance Transform for Tubular Structure Segmentation in CT Scanscited by 52
  6. 2023NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mappingcited by 41
  7. 2020Point2Skeleton: Learning Skeletal Representations from Point Cloudscited by 37
  8. 2023Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-Number Fieldcited by 37
  9. 2022Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Pointscited by 31
  10. 2018PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstructioncited by 13
  11. 2023Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentationcited by 9
  12. 2022PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Netcited by 3

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Hierarchical Part-based Generative Model for Realistic 3D Blood Vesselcited by 4