3D point clouds are one of the most common representations for captured 3D data, offering the advantage of closely aligning with sensing technologies and providing an unbiased representation of a measured physical scene. Progressive compression of those point clouds is essential for real-world applications operating on networked infrastructures with restricted or variable bandwidth.
We contribute a novel approach that leverages a recursive binary space partition, where the partitioning planes are not axis-aligned and optimized via an entropy criterion. The planes are encoded via a novel adaptive quantization method combined with prediction. The input 3D point cloud is encoded as an interlaced stream of partitioning planes and number of points in the cells of the partition.
Compared to previous work, our approach offers improved rate-distortion performance, especially at very low bitrates. This is crucial for the interactive navigation of large 3D point clouds on heterogeneous networked infrastructures.
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Salle 4352 (ESIEE Paris)