Holistic 3D Scene Understanding for Self-Driving Cars
Zehao Yu (Ph.D. Student)
Holistic 3D scene understanding plays a critical role in self-driving cars. It involves several sub-tasks, such as geometric layout estimation, object detection, recognition, and tracking. While each sub-task may be solved independently, it would be beneficial to utilize the complementary nature of different sub-task and model the 3D scene in a holistic way. Therefore, this project will focus on research problems to achieve this goal, such as representing the 3D scene compactly and finding a unified representation that can model scene geometry, sematic, and dynamics, and effectively and efficiently infer such 3D scene representation sensor inputs.
|Primary Host:||Andreas Geiger (University of Tübingen & Max Planck Institute for Intelligent Systems)|
|Exchange Host:||Torsten Sattler (Czech Technical University)|
|PhD Duration:||01 September 2021 - 30 June 2025|
|Exchange Duration:||01 December 2022 - 31 May 2023 - Ongoing|