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Kasi-Viswanath's Projects

atom icon atom

Calibration tools for multi-sensor, multi-modal, robotic systems

contactpose icon contactpose

Large dataset of hand-object contact, hand- and object-pose, and 2.9 M RGB-D grasp images.

cvml_project icon cvml_project

Projects and application using computer vision and machine learning

deepcalib icon deepcalib

DeepCalib: A Deep Learning Approach for Automatic Intrinsic Calibration of Wide Field-of-View Cameras

detr icon detr

End-to-End Object Detection with Transformers

g-vom icon g-vom

G-VOM: A GPU Accelerated Voxel Off-Road Mapping System

jetson-inference icon jetson-inference

Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

mavs-examples icon mavs-examples

Example applications and documentation for the MSU Autonomous Vehicle Simulator

ml-on-mnist icon ml-on-mnist

Implementation of various ML algorithms on MNIST dataset.

mlnd-capstone icon mlnd-capstone

Lane Detection with Deep Learning - My Capstone project for Udacity's ML Nanodegree

nn-svg icon nn-svg

Publication-ready NN-architecture schematics.

offseg icon offseg

Official Implementation of OFFSEG: A Semantic Segmentation framework for Off-Road Driving.

ptgrey-cameras-with-python icon ptgrey-cameras-with-python

These files explain how to use 'ptgrey' cameras with python. I felt the need to make this because there is not even an introductory code (to take a picture using ptgrey cameras) available on internet. take_a_photo.py is the basic code people usually need but I have added links to a few possibly helpful websites.

rellis-3d icon rellis-3d

RELLIS-3D: A Multi-modal Dataset for Off-Road Robotics

robotics icon robotics

Python implementations of University of Pennsylvania Robotics Specialization, and more.

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

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