Position adaptive residual block and knowledge complement strategy for point cloud analysis

Abstract

Due to the sparsity, irregularity and disorder of the point cloud, the tasks related to it are full of challenges. Exploring local geometric patterns and multi-scale features is effective for point cloud understanding, and promising results have been achieved. In this paper, we present a Position Adaptive Residual Block, namely PARB, for the first time. It can carry out powerful geometric signal description and feature learning. Starting from this module, we propose two extensions. First, a Position Adaptive Residual Network, called PARNet, is derived by utilizing PARB. Second, PARB can be regarded as a plug-and-play module embedded in MLP-based networks, which can remarkably enhance the performance of the backbone. We also introduce an efficient Knowledge Complement Strategy, which is part of the PARNet architecture, to make the framework perform better. Extensive experimental results on challenging benchmarks demonstrate that our PARNet delivers the new state-of-the-art on ShapeNet-Part and achieves competitive performance on ModelNet40.