Point cloud restoration via 2D projection and inpainting

Adrian Mai; Mark Bilinski; Raymond Provost; Michael Hess

doi:10.1117/12.2638513

26 October 2022 Point cloud restoration via 2D projection and inpainting

Adrian Mai, Mark Bilinski, Raymond Provost, Michael Hess

Proceedings Volume 12267, Image and Signal Processing for Remote Sensing XXVIII; 1226706 (2022) https://doi.org/10.1117/12.2638513
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

The presence of noise, displacement of points, and empty spots in a raw Light Detection and Ranging (LiDAR) point cloud are common phenomena caused by reflective surfaces or objects. Typical approaches to solve this problem are either avoid or cover the reflective areas or to manually remove the erroneous data in post processing. This can help clean the point cloud structure but will cause sparsity issues. To combat this, in this paper, we introduce a two-step process to perform point cloud restoration. Instead of removing noise, this approach can restore the points to the closest surface which they may belong to. Next, to fill out empty spots, we introduce a technique called point cloud inpainting, which involves interpolating points in 2D then mapping it back to 3D for flat surfaces. The point cloud then becomes more photorealistic and easier to use for other computer vision tasks.

Citation Download Citation

Adrian Mai, Mark Bilinski, Raymond Provost, and Michael Hess "Point cloud restoration via 2D projection and inpainting", Proc. SPIE 12267, Image and Signal Processing for Remote Sensing XXVIII, 1226706 (26 October 2022); https://doi.org/10.1117/12.2638513

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