This repository, contains my academic work for the Fall 2023 CMSC828I course. It includes assignments, projects, and relevant documentation covering various aspects of computer vision and recognition.

This repo contains the work done for CMSC 828I.It showcases a basic concept of a Implicit Neural Representation for a Single Image

Blur decomposition based on coded exposure photography and implicit neural representation of videos

Implementation of two phase field approaches for the surface reconstruction problem and shape space learning. One based of the Modica-Mortola theorem and the other based on Ambrosio-Tortorelli

End-to-End Framework for Continuous Space-Time Super-Resolution on Remote Sensing data.

[CVPR 2024 Highlight] LMF (Latent Modulated Function for Computational Optimal Continuous Image Representation)

[AAAI-2024] Pytorch implementation of "ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field"

Official code of UFORecon (CVPR 2024)

Repository containing code for Siamese SIREN: Audio Compression with Implicit Neural Representations. Published as a workshop paper at ICML 2023 neural compression workshop.

[ECCV'22] "Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space"

Official Code for "Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields"

Machine Learning Framework with Automatic Differentiation and Cuda Acceleration in C++

SAD-SLAM: Sign-Agnostic Dynamic Simultaneous Localization and Mapping

Pytorch3d rendering an visualization basics

With INR, we parameterize some signal (in our case images) with a neural network (in this assignment, we will use a basic feed-forward network).

A PyTorch implementation of "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation" by Park et al

FENRI (Fiber Orientations from Explicit Neural Representations) Implementation Repository

[ICCV 2023] Curvature-Aware Training for Coordinate Networks

ADL4CV 2023 project

[CVPR 2024 Highlight] Boosting Neural Representations for Videos with a Conditional Decoder