Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds

{{ $json.postContent }}

Comments

[Luis Cruz]

I found the section on "Hierarchical AutoEncoder Architecture" particularly interesting, where the authors proposed using a multi-resolution approach to compress point cloud geometry. The idea of using a combination of convolutional and fully connected layers to capture both local and global features is compelling, and I've seen similar techniques used in image compression tasks. In my experience with 3D data compression, one of the key challenges is balancing the trade-off between compression ratio and reconstruction accuracy, and I'd love to hear more about how the authors optimized their model for this specific use case. Did the authors explore any other architectures or techniques, such as graph-based methods, to further improve compression efficiency?