Authors: Jianfeng Cui & Aakaash Radhoe
Introduction and Motivation
In this project, we designed a strategy for solving the 3D object detection problem for a robot named TIAGo [1], which is working in a retail store. The whole working scenario for TIAGo is to recognize, pick up and deliver the products to the customers, so this project serves as its perception module. It will be able to recognize the target products appearing in the image view, calculate its 3D pose and deliver this information to other modules. …
Authors: Jeroen Dekker & Aakaash Radhoe
In this blog, we will reproduce the results of Tables 1 & 2 of the original paper. In this reproduction, we try to get a similar result as found in the paper. This is done from scratch since there was no code or data shared by the authors of the paper. So we had to search for the datasets (CFD and Crack500) online and found these at: https://drive.google.com/drive/folders/1y9SxmmFVh0xdQR-wdchUmnScuWMJ5_O-. The U-Net-based Convolutional Network was made by us, with the help of the PyTorch documentation. For the reproducibility project, we chose to work with PyTorch as…
