Depth estimation and human detection with a monocular phone camera
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Updated
Oct 12, 2021 - Jupyter Notebook
Depth estimation and human detection with a monocular phone camera
A Docker container for determining depth maps of 2D images using MiDaS
Model compression methods applied to the monocular depth estimation network by Godard et al.
A CS 410: Computational Imaging term project aimed at accurately predicting depth from a single 2D image.
Website for our 3DV 2021 paper "Self-Supervised Monocular Scene Decomposition and Depth Estimation".
Blog article on the paper 3D Packing for Self-Supervised Monocular Depth Estimation published by Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos and Adrien Gaidon
This project implements a simple U-Net for monocular depth estimation leveraging NYU Depth V2 dataset.
ML research internship project, for the Bachellor's Degree in Computer Science @ University of Padua
Diploma Thesis Documents - Electrical and Computer Engineering
monocular depth estimation using latent diffusion models
Software for an assistive device for low-vision and blind individuals. Designed for the Raspberry Pi Compute Module 4 on a custom carryout board.
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training
NAIL summer internship project creating a relatable visual demonstration of AI for visitors to the AI Lab.
Website for our publication, entitled "ConvDepth: Self-Supervised Monocular Depth Estimation for Autonomous Driving"
An unofficial PyTorch implementation of Self-supervised Depth Estimation from Spectral Consistency and Novel View Synthesis
The results of the project are attached in test_imgae_out.jpg and test_video.mp4. Please read the read.md file for description of project.
3D obstacle avoidance using perception
Implementation of Algorithms for estimating the defocus map from a single defocused image source using image processing techniques and mathematical modelling.
This is the webpage of CVPR'2024 paper "ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation"
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