MaWeakBboxPrediction

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Master's thesis

Thesis by: Thomas Schmid
Advisor: Nassir Navab
Supervision by: Iro Laina, Federico Tombari
Due date: 15.12.2018

Abstract

The performance of AI-driven systems highly depends on training data which is (usually) manually generated and requires money and time whilst being human error-prone. This thesis will address the problem of automatic 3D object detection of pedestrians and cars in an autonomous driving setting. We will exploit deep 2D mask proposals from RGB data (e.g. Fast/Faster R-CNN). In combination with LiDAR? data and classical depth completion and clustering, our goal is to bridge the gap to 3D allowing the generation of 3D bounding boxes in a weakly supervised setup.


Students.ProjectForm
Title: 3D Bounding Box Prediction from RGB and LiDAR? Data Using 2D Proposals
Abstract: The performance of AI-driven systems highly depends on training data which is (usually) manually generated and requires money and time whilst being human error-prone. This thesis will address the problem of automatic 3D object detection of pedestrians and cars in an autonomous driving setting. We will exploit deep 2D mask proposals from RGB data (e.g. Fast/Faster R-CNN). In combination with LiDAR? data and classical depth completion and clustering, our goal is to bridge the gap to 3D allowing the generation of 3D bounding boxes in a weakly supervised setup.
Student: Thomas Schmid
Director: Nassir Navab
Supervisor: Iro Laina, Federico Tombari
Type: Master Thesis
Area: Computer Vision
Status: finished
Start: 01.04.2018
Finish: 15.12.2018
Thesis (optional):  
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Edit | Attach | Refresh | Diffs | More | Revision r1.2 - 15 Dec 2018 - 11:33 - IroLaina