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

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3D Object Detection and Flow Estimation in Dynamic Surgical Environments


3D object detection is the task of detecting 3D bounding box locations of objects from images or 3D environments. Whereas flow estimation is the extraction motion flow of the scene. In a temporal 3D environment, object detection and flow estimation is closely tied to each other, and can be used for reasoning from one another.
This IDP aims to connect these together, and establish a low-shot or self-supervised training procedure, which can be tremendously useful in the scenarios where large data collection is not easy, eg. in surgical environments.
One student will work on creating the 3D object detection pipeline from a state-of-the-art model, and one student will establish the 3D flow estimation. These two then be tied together by defining the relevant loss functions and dependencies.

Scene Flow Estimation 3D Object Detection


Liu, Xingyu, Charles R. Qi, and Leonidas J. Guibas. "Flownet3d: Learning scene flow in 3d point clouds." CVPR 2019

Qi, Charles R., et al. "Deep hough voting for 3d object detection in point clouds." ICCV 2019.

Title: 3D Object Detection and Flow Estimation in Dynamic Surgical Enviroments
Student: Dilara Gokay, Enis Simsar
Director: Nassir Navab, Federico Tombari
Supervisor: Mahdi Saleh , Evin Pınar Örnek
Type: IDP
Area: Segmentation, Machine Learning, Computer Vision
Status: running
Start: 09.2020
Finish: 03.2021
Thesis (optional):  

Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 01 Nov 2020 - 15:57 - EvinPinarOrnek