Year: 2024
Type: conference-paper
Abstract: Convolution, a fundamental operation in deep learning for structured grid data like images, cannot be directly applied to point clouds due to their irregular and unordered nature.Many approaches in literature that perform convolution on point clouds achieve this by designing a convolutional operator from scratch, often with little resemblance to the one used on images.We present two point cloud convolutions that naturally follow from the convolution in its standard definition popular with images.We do so by relaxing the indexing of the kernel weights with a "soft" dictionary that resembles the attention mechanism of the transformers.Finally, experimental results demonstrate the effectiveness of the proposed relaxations on two benchmark point cloud classification tasks. Generalizing Convolution to Point CloudsGiven the tensors
Authors: Davide Bacciu, Francesco Landolfi
Institution: University of Pisa
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4402365136