Geometric Understanding of Sketches
Raghav Brahmadesam Venkataramaiyer

TL;DR
This thesis introduces two methods for geometric understanding of sketches: one for interpreting 2D line drawings as graphs for robotic replication, and another for inferring 3D object details from sketches using deep learning.
Contribution
It presents a novel pipeline for graph-based interpretation of sketches and a data-driven approach for 3D understanding without explicit 3D data, validated through experiments and user studies.
Findings
Accurate vertex estimation with deep CNNs and clustering.
Successful robotic replication of sketches from interpreted graphs.
Effective 3D contour completion from sketches using adversarial training.
Abstract
Sketching is used as a ubiquitous tool of expression by novices and experts alike. In this thesis I explore two methods that help a system provide a geometric machine-understanding of sketches, and in-turn help a user accomplish a downstream task. The first work deals with interpretation of a 2D-line drawing as a graph structure, and also illustrates its effectiveness through its physical reconstruction by a robot. We setup a two-step pipeline to solve the problem. Formerly, we estimate the vertices of the graph with sub-pixel level accuracy. We achieve this using a combination of deep convolutional neural networks learned under a supervised setting for pixel-level estimation followed by the connected component analysis for clustering. Later we follow it up with a feedback-loop-based edge estimation method. To complement the graph-interpretation, we further perform data-interchange to…
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Taxonomy
Topics3D Shape Modeling and Analysis · Advanced Vision and Imaging · Computer Graphics and Visualization Techniques
