Rethinking Design and Evaluation of 3D Point Cloud Segmentation Models

Abstract

Currently, the use of 3D point clouds is rapidly increasing in many engineering fields, such as geoscience and manufacturing. Various studies have developed intelligent segmentation models providing accurate results, while only a few of them provide additional insights into the efficiency and robustness of their proposed models. The process of segmentation in the image domain has been studied to a great extent and the research findings are tremendous. However, the segmentation analysis with point clouds is considered particularly challenging due to their unordered and irregular nature. Additionally, solving downstream tasks with 3D point clouds is computationally inefficient, as point clouds normally consist of thousands or millions of points sparsely distributed in 3D space. Thus, there is a significant need for rigorous evaluation of the design characteristics of segmentation models, to be effective and practical. Consequently, in this paper, an in-depth analysis of five fundamental and representative deep learning models for 3D point cloud segmentation is presented. Specifically, we investigate multiple experimental dimensions, such as accuracy, efficiency, and robustness in part segmentation (ShapeNet) and scene segmentation (S3DIS), to assess the effective utilization of the models. Moreover, we create a correspondence between their design properties and experimental properties. For example, we show that convolution-based models that incorporate adaptive weight or position pooling local aggregation operations achieve superior accuracy and robustness to point-wise MLPs, while the latter ones show higher efficiency in time and memory allocation. Our findings pave the way for an effective 3D point cloud segmentation model selection and enlighten the research on point clouds and deep learning.