PointResNet: Residual Network for 3D Point Cloud Segmentation and Classification

TL;DR

PointResNet introduces a residual network architecture that directly processes 3D point clouds for segmentation and classification, achieving superior or comparable results to existing methods by preserving structural features.

Contribution

It presents a novel deep neural network architecture using residual blocks and MLPs that directly processes raw 3D point clouds for improved segmentation and classification.

Findings

Achieves state-of-the-art results in segmentation tasks.

Provides comparable performance in classification tasks.

Effectively preserves structural features in point clouds.

Abstract

Point cloud segmentation and classification are some of the primary tasks in 3D computer vision with applications ranging from augmented reality to robotics. However, processing point clouds using deep learning-based algorithms is quite challenging due to the irregular point formats. Voxelization or 3D grid-based representation are different ways of applying deep neural networks to this problem. In this paper, we propose PointResNet, a residual block-based approach. Our model directly processes the 3D points, using a deep neural network for the segmentation and classification tasks. The main components of the architecture are: 1) residual blocks and 2) multi-layered perceptron (MLP). We show that it preserves profound features and structural information, which are useful for segmentation and classification tasks. The experimental evaluations demonstrate that the proposed model produces the best results for segmentation and comparable results for classification in comparison to the conventional baselines.