Learning to Detect Geometric Structures from Images for 3D Parsing

Abstract

Recovering 3D geometries of scenes from 2D images is one of the most fundamental and challenging problems in computer vision. On one hand, traditional geometry-based algorithms such as SfM and SLAM are fragile in certain environments, and the resulting noisy point-clouds are hard to process and interpret. On the other hand, recent learning-based 3D-understanding neural networks parse scenes by extrapolating patterns seen in the training data, which often have limited generalizability and accuracy.

In my dissertation, I try to address these shortcomings and combine the advantage of geometry-based and data-driven approaches into an integrated framework. More specifically, I have applied learning-based methods to extract high-level geometric structures from images and use them for 3D parsing. To this end, I have designed specialized neural networks that understand geometric structures such as lines, junctions, planes, vanishing points, and symmetry, and detect them from images accurately; I have created large-scale 3D datasets with structural annotations to support data-driven approaches; and I have demonstrated how to use these high-level abstractions to parse and reconstruct scenes. By combining the power of data-driven approaches and geometric principles, future 3D systems are becoming more accurate, reliable, and easier to implement, resulting in clean, compact, and interpretable scene representations.

Publication
Doctoral Dissertation

Dissertation Talk

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