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Depth Completion Using Advanced Anisotropic Diffusion Techniques
In this project, we aim to develop a depth completion framework based on the anisotropic diffusion prior.
Keywords: Depth Completion, Deep Learning, Machine Learning, Computer Vision
Depth completion is a computer vision task where a scene is recorded with a sparse set of depth measurements (for example with LIDAR) and an RGB camera, and one needs to complete the unobserved pixels. Depth completion task are useful for various domains like autonomous driving, AR/VR, and remote sensing.
Recently, anisotropic diffusion has proven effective for guided super-resolution, especially when combined with deep learning [1]. In this project, we plan to extend the anisotropic diffusion concept to the task of depth completion.
[1] https://openaccess.thecvf.com/content/CVPR2023/html/Metzger_Guided_Depth_Super-Resolution_by_Deep_Anisotropic_Diffusion_CVPR_2023_paper.html
Image credits: https://paperswithcode.com/task/depth-completion
Depth completion is a computer vision task where a scene is recorded with a sparse set of depth measurements (for example with LIDAR) and an RGB camera, and one needs to complete the unobserved pixels. Depth completion task are useful for various domains like autonomous driving, AR/VR, and remote sensing. Recently, anisotropic diffusion has proven effective for guided super-resolution, especially when combined with deep learning [1]. In this project, we plan to extend the anisotropic diffusion concept to the task of depth completion.
The student is expected to (1) perform a review of the most recent prior art in the domain, (2) reproduce given code bases (inference and training), (3) propose experiment objectives and code changes, and (4) organize findings in the final report. Stretch goals include submission to a top-tier conference (CVPR, ICCV, ECCV) and organizing the proposed solution into a high-impact utility, such as a code repository or a Python package.
Settings for applications
Python, PyTorch, Linux shell;
MSc-level knowledge of (deep) machine learning and computer vision/image analysis
The student is expected to (1) perform a review of the most recent prior art in the domain, (2) reproduce given code bases (inference and training), (3) propose experiment objectives and code changes, and (4) organize findings in the final report. Stretch goals include submission to a top-tier conference (CVPR, ICCV, ECCV) and organizing the proposed solution into a high-impact utility, such as a code repository or a Python package. Settings for applications Python, PyTorch, Linux shell; MSc-level knowledge of (deep) machine learning and computer vision/image analysis
Nando Metzger (nando.metzger@geod.baug.ethz.ch), Photogrammetry and Remote Sensing, ETH Zürich
Alex Becker (alexander.becker@geod.baug.ethz.ch), Photogrammetry and Remote Sensing, ETH Zürich
Nando Metzger (nando.metzger@geod.baug.ethz.ch), Photogrammetry and Remote Sensing, ETH Zürich Alex Becker (alexander.becker@geod.baug.ethz.ch), Photogrammetry and Remote Sensing, ETH Zürich