|M.Sc Student||Wolff Adam|
|Subject||Image-Guided Depth Sampling and Reconstruction|
|Department||Department of Electrical Engineering||Supervisor||Professor Guy Gilboa|
|Full Thesis text|
Depth acquisition, based on active illumination, is essential for autonomous and robotic navigation. LiDARs (Light Detection And Ranging) with mechanical, fixed, sampling templates are commonly used in today's autonomous vehicles. An emerging technology, based on solid-state depth sensors, with no mechanical parts, allows fast, adaptive, programmable scans.
In this research, we investigate the topic of adaptive, image-driven, sampling and reconstruction strategies. First, we formulate a piece-wise planar depth model with several tolerance parameters and estimate its validity for indoor and outdoor scenes. Our model and experiments predict that, in the optimal case, about 20-60 piece-wise planar structures can approximate well a depth map. This translates to a depth-to-image sampling ratio of about 1/1200. Later, we propose a simple, generic, sampling and reconstruction algorithm, based on super-pixels. We reach a sampling rate which is still far from the optimal case. However, our sampling improves grid and random sampling, consistently, for a wide variety of reconstruction methods. Moreover, our proposed reconstruction achieves state-of-the-art results, compared to image-guided depth completion algorithms, reducing the required sampling rate by a factor of 3-4. A single-pixel depth camera built in our lab illustrates the concept.