mlsanityData to 14 Oct 2026

Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects

arXiv 2105.06858 ↗First posted 14 May 2021cs.GR cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

We propose Fit4CAD, a benchmark for the evaluation and comparison of methods for fitting simple geometric primitives in point clouds representing CAD objects. This benchmark is meant to help both method developers and those who want to identify the best performing tools. The Fit4CAD dataset is composed by 225 high quality point clouds, each of which has been obtained by sampling a CAD object. The way these elements were created by using existing platforms and datasets makes the benchmark easily expandable. The dataset is already split into a training set and a test set. To assess performance and accuracy of the different primitive fitting methods, various measures are defined. To demonstrate the effective use of Fit4CAD, we have tested it on two methods belonging to two different categories of approaches to the primitive fitting problem: a clustering method based on a primitive growing framework and a parametric method based on the Hough transform.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 7 of its 31 references that are in the map. First posted on arXiv 14 May 2021.

Builds on

7 of 31 references.

References · 7

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 341
  3. 2016Thingi10K: A Dataset of 10,000 3D-Printing Modelscited by 183
  4. 2016Learning Shape Abstractions by Assembling Volumetric Primitivescited by 181
  5. 2020Kaggle forecasting competitions: An overlooked learning opportunitycited by 56
  6. 2018Parsing Geometry Using Structure-Aware Shape Templatescited by 16
  7. 2016Approximate implicitization using linear algebracited by 3

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.