Optimized CNNs for Rapid 3D Point Cloud Object Recognition

TL;DR

This paper presents a novel sparse CNN architecture with an $ abla$1 regularization technique for efficient 3D object detection in point clouds, achieving superior accuracy and speed on the MVTec 3D-AD benchmark.

Contribution

It introduces a sparse convolutional layer design with $ abla$1 regularization, improving 3D point cloud object recognition efficiency and accuracy over prior methods.

Findings

Outperforms previous state-of-the-art in 3D object detection

Maintains competitive processing speeds for real-time use

Demonstrates effectiveness on the MVTec 3D-AD benchmark

Abstract

This study introduces a method for efficiently detecting objects within 3D point clouds using convolutional neural networks (CNNs). Our approach adopts a unique feature-centric voting mechanism to construct convolutional layers that capitalize on the typical sparsity observed in input data. We explore the trade-off between accuracy and speed across diverse network architectures and advocate for integrating an $\mathcal{L}_1$ penalty on filter activations to augment sparsity within intermediate layers. This research pioneers the proposal of sparse convolutional layers combined with $\mathcal{L}_1$ regularization to effectively handle large-scale 3D data processing. Our method's efficacy is demonstrated on the MVTec 3D-AD object detection benchmark. The Vote3Deep models, with just three layers, outperform the previous state-of-the-art in both laser-only approaches and combined laser-vision methods. Additionally, they maintain competitive processing speeds. This underscores our approach's capability to substantially enhance detection performance while ensuring computational efficiency suitable for real-time applications.