An improved fused feature residual network for 3D point cloud data.

Point clouds have evolved into one of the most important data formats for 3D representation. It is becoming more popular as a result of the increasing affordability of acquisition equipment and growing usage in a variety of fields. Volumetric grid-based approaches are among the most successful models for processing point clouds because they fully preserve data granularity while additionally making use of point dependency. However, using lower order local estimate functions to close 3D objects, such as the piece-wise constant function, necessitated the use of a high-resolution grid in order to capture detailed features that demanded vast computational resources. This study proposes an improved fused feature network as well as a comprehensive framework for solving shape classification and segmentation tasks using a two-branch technique and feature learning. We begin by designing a feature encoding network with two distinct building blocks: layer skips within, batch normalization (BN), and rectified linear units (ReLU) in between. The purpose of using layer skips is to have fewer layers to propagate across, which will speed up the learning process and lower the effect of gradients vanishing. Furthermore, we develop a robust grid feature extraction module that consists of multiple convolution blocks accompanied by max-pooling to represent a hierarchical representation and extract features from an input grid. We overcome the grid size constraints by sampling a constant number of points in each grid using a simple K-points nearest neighbor (KNN) search, which aids in learning approximation functions in higher order. The proposed method outperforms or is comparable to state-of-the-art approaches in point cloud segmentation and classification tasks. In addition, a study of ablation is presented to show the effectiveness of the proposed method.