Overview
Modern 3D reconstruction techniques rely on pixel consistency across images to recover 3D geometry from 2D images. These methods include NeRFs [1] and Gaussian Splatting [2], which are the most accurate techniques in current state-of-the-art. However, they struggle on inputs with little contrast, objects which are transparent or reflective, and views which are too few or with limited angle.
Objectives
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Collect inputs that are challenging for 3D reconstruction
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Quantitatively evaluate these limitations and understand them with ablation studies
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Compare various state of the art methods
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Explore ways to mitigate these limitations
Prerequisites
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Python proficiency, familiarity with Pytorch
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Experience with running external projects in bash
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Understanding of 3D camera model is a plus
Contact
Both bachelor and master students are welcome to apply. This project will be conducted in collaboration with CVLab. Contact [email protected] for more information.
References
[1] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, ECCV 2020
[2] 3D Gaussian Splatting for Real-Time Radiance Field Rendering, SIGGRAPH 2023