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Generative Models for 3D Shape Completion
Zdravecký, Peter ; Španěl, Michal (oponent) ; Kubík, Tibor (vedoucí práce)
In many real-world scenarios, scanned 3D models contain missing parts due to occlusion, scanning errors, or the incomplete nature of the data itself. The goal of this work is to create an automated process for 3D shape completion using a supervised deep learning-based method. The proposed solution is based on the prior work of DiffComplete, which uses a diffusion-based model operating over distance field representation and handles the task as a generative problem. The results showed a high capability of this model with an 81.6 IoU metric on the custom-prepared test set of furniture objects. The model also demonstrates strong generalization capabilities on shapes that are out of the training distribution (average 70.9 IoU metric). Apart from more detailed data-centric experiments, this work further extends current state-of-the-art in two ways. Firstly, it addresses the most crucial shortcoming, expensive computation, by processing the input in a low-resolution domain. Secondly, it utilizes user input (Region of Interest), which gives the user more control over generation in ambiguous scenarios.
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