  In this paper we present results of performance evaluation of S3DCNN - a
Sparse 3D Convolutional Neural Network - on a large-scale 3D Shape benchmark
ModelNet40, and measure how it is impacted by voxel resolution of input shape.
We demonstrate comparable classification and retrieval performance to
state-of-the-art models, but with much less computational costs in training and
inference phases. We also notice that benefits of higher input resolution can
be limited by an ability of a neural network to generalize high level features.
