Enhanced 3D Point Cloud Object Detection with Iterative Sampling and Clustering Algorithms

Existing state-of-the-art object detection networks for 3D point clouds provide bounding box results directly from 3D data, without reliance on 2D detection methods. While state-of-the-art accuracy and mAP (mean average precision) results are achieved by GroupFree3D, MLCVNet and VoteNet methods for the SUN RGBD and ScanNet V2 datasets, challenges remain in translating these methods across multiple datasets for a variety of applications. These challenges arise due to the irregularity, sparsity and noise present in point clouds which hinder object detection networks from extracting accurate features and bounding box results. In this paper, we extend existing state-of-the-art 3D point cloud object detection methods to include filtering of outlier data via iterative sampling and accentuate feature learning via clustering algorithms. Specifically, the use of RANSAC allows for the removal of outlier points from the dataset scenes and the integration of DBSCAN, K-means, BIRCH and OPTICS clustering algorithms allows the detection networks to optimise the extraction of object features. We demonstrate a mean average precision improvement for some classes of the SUN RGB-D validation dataset through the use of iterative sampling against current state-of-the-art methods while demonstrating a consistent object accuracy of above 99.1%. The results of this paper demonstrate that combining iterative sampling with current state-of-the-art 3D point cloud object detection methods can improve accuracy and performance while reducing the computational size.