Momen(e)t: Flavor the Moments in Learning to Classify Shapes
This work addresses shape classification for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the problem of classifying 3D shapes from point clouds by proposing to add polynomial functions of coordinates to account for higher-order moments, achieving state-of-the-art results on two benchmarks with improved efficiency in memory and computation.
A fundamental question in learning to classify 3D shapes is how to treat the data in a way that would allow us to construct efficient and accurate geometric processing and analysis procedures. Here, we restrict ourselves to networks that operate on point clouds. There were several attempts to treat point clouds as non-structured data sets by which a neural network is trained to extract discriminative properties. The idea of using 3D coordinates as class identifiers motivated us to extend this line of thought to that of shape classification by comparing attributes that could easily account for the shape moments. Here, we propose to add polynomial functions of the coordinates allowing the network to account for higher order moments of a given shape. Experiments on two benchmarks show that the suggested network is able to provide state of the art results and at the same token learn more efficiently in terms of memory and computational complexity.