research-article

Reconstruction of Machine-Made Shapes from Bitmap Sketches

Authors:

Cedric Martens,

Paul G. Kry, and

Mikhail BessmeltsevAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 42, Issue 6

Article No.: 268, Pages 1 - 16

https://doi.org/10.1145/3618361

Published: 05 December 2023 Publication History

Abstract

We propose a method of reconstructing 3D machine-made shapes from bitmap sketches by separating an input image into individual patches and jointly optimizing their geometry. We rely on two main observations: (1) human observers interpret sketches of man-made shapes as a collection of simple geometric primitives, and (2) sketch strokes often indicate occlusion contours or sharp ridges between those primitives. Using these main observations we design a system that takes a single bitmap image of a shape, estimates image depth and segmentation into primitives with neural networks, then fits primitives to the predicted depth while determining occlusion contours and aligning intersections with the input drawing via optimization. Unlike previous work, our approach does not require additional input, annotation, or templates, and does not require retraining for a new category of man-made shapes. Our method produces triangular meshes that display sharp geometric features and are suitable for downstream applications, such as editing, rendering, and shading.

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Cited By

Tono AHuang HAgrawal AFischer M(2024)Vitruvio: Conditional variational autoencoder to generate building meshes via single perspective sketchesAutomation in Construction10.1016/j.autcon.2024.105498166(105498)Online publication date: Oct-2024
https://doi.org/10.1016/j.autcon.2024.105498

Index Terms

Reconstruction of Machine-Made Shapes from Bitmap Sketches
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling

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ACM Transactions on Graphics Volume 42, Issue 6

December 2023

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ISSN:0730-0301

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DOI:10.1145/3632123

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Published: 05 December 2023

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Cited By

Tono AHuang HAgrawal AFischer M(2024)Vitruvio: Conditional variational autoencoder to generate building meshes via single perspective sketchesAutomation in Construction10.1016/j.autcon.2024.105498166(105498)Online publication date: Oct-2024
https://doi.org/10.1016/j.autcon.2024.105498

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