CMU

About The Project Implemented various algorithm to conduct F1Tenth Racing including pure pursuit, RRT, and RRT*. Built With Python NumPy Emergency Braking Implemented Emergency Braking for the car using LiDAR scans and calculating the instantaneous Time to Collision (iTTC). Wall Following Implemented PID control in the time domain to give commands to the vehicle to follow the wall. Used LiDAR data to calculate the distance from the wall. $$ u(t)=K_{p}e(t)+K_{i}\int_{0}^{t}e(t^{\prime})dt^{\prime}+K_{d}\frac{d}{dt}(e(t)) $$...

About The Project This is a soap slipping, Lego stepping, and trash bin wearing sleep helping game which allow maximum of four players to play at the same time. Go grab 2 or 3 friends and play with them! This is a funny, easy and a BIT of WEIRD game! Link to Download Download from official Site Built With CPP OpenGL Blender Team members Sizhe Chen, Ray Huang, Jiyoon Park, Luka Liu

About The Project Developed indi games using CPP. Built With CPP OpenGL Blender Tobby’s Math Class You are Tobby’s gardian for today’s math class. Tobby has to navigate around the classroom and collect materials to meet his target, but once Tobby eats a number, it does not appear again. (Also, remeber you are Tobby’s gardian, which means you have to be responsible for remebering his collections : ) Find the Real Tobby “Find the Real Tobby” innovatively blends consistency in character movement with a strategy of distinguishing the real Tobby among fakes, creating a captivating and challenging gameplay concept....

16-824 Visual Learning and Recognition: Homework 3 · Spring 2023 · GITHUB About The Project Implemented and trained different components of a Transformer decoder for image captioning using a subset of the COCO dataset. Additionally, a Vision Transformer (ViT) was implemented for classification on CIFAR10. Built With Python NumPy Pytorch Results For the entire report, please refer to the Documentation

3D Reconstruction 16-824 Visual Learning and Recognition: Homework 2 · Spring 2023 GITHUB About The Project Implemented and trained Generative Adversarial Networks (GAN) on the CUB 2011 Dataset. The primary goal was to implement GANs following provided instructions, with an emphasis on achieving specific Final FID (Fréchet Inception Distance) scores for different GAN variants: Vanilla GAN, LS-GAN, and WGAN-GP. Built With Python NumPy Pytorch Results Vanilla GAN: Final FID 71.95487635262793...

3D Reconstruction 16-720 Computer Vision: Homework 4 · Fall 2022 GITHUB About The Project Implemented an algorithm to reconstruct a 3D point cloud from a pair of images taken at different angles. Used the 8-point/7-point algorithm and triangulation to find and visualize 3D locations of corresponding image points. Built With Python NumPy Results For the entire report, please refer to the Documentation The Eight Point Algorithm for calculating the fundamental matrix The Seven Point Algorithm for calculating the fundamental matrix 3D Visualization of point cloud Bundle Adjustment using RANSAC Multi View Keypoint Reconstruction

Augmented Reality with Planar Homographies 16-720 Computer Vision: Homework 3 · Fall 2022 GITHUB About The Project Implementing an AR application step by step using planar homographies. Finding point correspondences between two images and use these to estimate the homography between them. Then using this homography to warp images and finally implement it on AR applications. Built With Python NumPy Results For the entire report, please refer to the Documentation...

Lucas-Kanade Tracking 16-720 Computer Vision: Homework 2 · Fall 2022 GITHUB About The Project Implement a simple Lucas-Kanade (LK) tracker with a single template. And implement a motion subtraction method to track moving pixels in a scene. Finally, efficient tracking using inverse composition. Built With Python NumPy Results For the entire report, please refer to the Documentation Lucas-Kanade Tracking with One Single Template Lucas-Kanade Tracking with Template Correction Moving Object Detection Track movement in video

Spatial Pyramid Matching for Scene Classification 16-720 Computer Vision: Homework 1 · Fall 2022 GITHUB About The Project Implementation of a scene classification system that uses bag-of-words approach with its spatial pyramid extension. Built With Python NumPy Pipeline Results For the entire report, please refer to the Documentation Extracting Filter Responses Visualization of wordmaps Building A Model of the Visual Word Final accuracy of classification