Denoising of 3D point clouds constructed from light fields

Christian Galea (1) , Christine Guillemot (1)

1 Sirocco - Analysis representation, compression and communication of visual data

Light fields are 4D signals capturing rich information from a scene. The availability of multiple views enables scene depth estimation, that can be used to generate 3D point clouds. The constructed 3D point clouds, however, generally contain distortions and artefacts primarily caused by inaccuracies in the depth maps. This paper describes a method for noise removal in 3D point clouds constructed from light fields. While existing methods discard outliers, the proposed approach instead attempts to correct the positions of points, and thus reduce noise without removing any points, by exploiting the consistency among views in a light-field. The proposed 3D point cloud construction and denoising method exploits uncertainty measures on depth values. We also investigate the possible use of the corrected point cloud to improve the quality of the depth maps estimated from the light field.

Mots clés: Light fields; point clouds; depth estimation; denoising

Domaines: Traitement du signal et de l'image [eess.SP]

Christian Galea, Christine Guillemot. Denoising of 3D point clouds constructed from light fields. ICASSP 2019 - IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2019, Brighton, United Kingdom. pp.1882-1886, ⟨10.1109/ICASSP.2019.8683548⟩. ⟨hal-02116377⟩