Affiliation:
1. Department of Computer Science and Engineering, IIT Bombay
Abstract
We present a deep neural framework that allows users to create surfaces from a stream of sparse 3D sketch strokes. Our network consists of a global surface estimation module followed by a local surface refinement. This facilitates in the incremental prediction of surfaces. Thus, our proposed method works with 3D sketch strokes and estimate a surface interactively in real time. We compare the proposed method with various state-of-the-art methods and show its efficacy for surface fitting. Further, we integrate our method into an existing Blender based 3D content creation pipeline to show its usefulness in 3D modelling.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
2 articles.
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1. Learning Assisted Interactive 3D modelling from 3D sketches;SIGGRAPH Asia 2023 Doctoral Consortium;2023-11-28
2. Synthesizing 3D VR Sketch Using Generative Adversarial Neural Network;Proceedings of the 2023 7th International Conference on Big Data and Internet of Things;2023-08-11