Sketch-based Normal Map Generation with Geometric Sampling
Not on the map. Why?
Abstract (arXiv)
Normal map is an important and efficient way to represent complex 3D models. A designer may benefit from the auto-generation of high quality and accurate normal maps from freehand sketches in 3D content creation. This paper proposes a deep generative model for generating normal maps from users sketch with geometric sampling. Our generative model is based on Conditional Generative Adversarial Network with the curvature-sensitive points sampling of conditional masks. This sampling process can help eliminate the ambiguity of generation results as network input. In addition, we adopted a U-Net structure discriminator to help the generator be better trained. It is verified that the proposed framework can generate more accurate normal maps.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 23 Apr 2021.
Builds on
10 of 18 references.
References · 10
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
- 2017Wasserstein GANcited by 635
- 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
- 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
- 2014Designing Deep Networks for Surface Normal Estimationcited by 59
- 20173D Shape Reconstruction from Sketches via Multi-view Convolutional Networkscited by 44
- 2017DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modelingcited by 16
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1.
3 papers.