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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Combine Stereo and Lidar for Dense Depth Estimation |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2022 |
| Abstract Text | Estimating an accurate depth map is crucial for several robotics applications, especially autonomous cars. In this project we explore how to integrate stereo matching with Lidar information to produce an accurate depth map. Starting from the RAFT architecture, we obtain a model which is able to substantially improve its prediction accuracy given a small amount of Lidar points. These Lidar points are used both to initialize the disparity estimation and as a constant input to the recurrent layer in the proposed architecture. Additionally, we also handle the Lidar sparsity issue by adopting sparse convolution operation instead of working on standard CNN so that a model trained on sparse and cheap Lidar can be generalized to other types of Lidar. |
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