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autoregressive_image_completion

drill-defect-classification

This project focuses on detecting and analyzing wear in drill bits during the drilling process. It involves studying three types of drill wear (flank, chisel, and outer corner wear) along with a healthy drill condition, using four corresponding datasets. The goal is to determine the most effective strategy for identifying drill bit wear.

pressure-enthalpy-diagram

rover_segmentation_using_kmeans

The project is designed to segment an image into rover, background, and shadows using a stepwise image processing algorithms. This includes color space conversion, blurring, normalization, optional contrast enhancement, feature reshaping, k-means clustering, mask merging, and morphological refinement.

superpixelsegmentation_using_slic

The project aims to segment images into rover, background, and shadow. It starts with initial segmentation using SLIC and adaptive SLIC, followed by applying a Region Adjacency Graph (RAG). To address over-segmentation, Hierarchical Merging and Normalized Cuts are used.

surface_reconstruction_from_pointcloud

This project implements a neural network model for reconstructing 3D surfaces, based on the DeepSDF architecture from CVPR 2019. It uses a deep, fully-connected neural network (Decoder) to learn Signed Distance Functions (SDFs) from 3D point clouds.

text2shape

This project is a DL model that performs 3D shape (voxelization) generation. It takes text prompts and produces 3D shapes. The model is implemented using supervised learning and has been tested on pytorch.

vio-using-homography-matrix

This application uses homography matrix, extracted from consecutive frames of a monocular camera, and fuses this data with input from an inertial measurement unit (IMU). This fusion is employed to accurately estimate positional changes.