A Robust Point Cloud Analysis Framework Inspired By Primary Visual Cortex

2026-06-12

AI Summary

This work addresses the high energy consumption and poor robustness of conventional CNNs in point cloud analysis by proposing a brain-inspired neural network, DC-CCNN, and its enhanced variant, DC-CCNN++. Inspired by the primary visual cortex, the approach introduces dendritic connectivity and continuous coupling mechanisms into point cloud modeling for the first time, effectively integrating discrete and continuous representations. It further incorporates a neuro-inspired modulated readout module (NRMR) and a cortex-inspired progressive variability training strategy (CPVT). The model achieves near state-of-the-art performance on classification and part segmentation tasks while demonstrating significantly improved robustness under challenging conditions, including sparsity, occlusion, Gaussian and salt-and-pepper noise, and spatial transformations.

Abstract

Despite significant advancements in point cloud analysis, reducing energy consumption and improving robustness remain understudied, largely due to the inherent limitations of Convolutional Neural Networks (CNNs). To address this issue, we draw inspiration from the primary visual cortex and propose a Dendritic-Connected Continuous-Coupled Neural Network (DC-CCNN), a novel Brain-Inspired Neural Network (BINN) architecture for point cloud analysis. By combining discrete and continuous encoding, our design replaces traditional Multilayer Perceptrons (MLPs) with more efficient and robust BINNs. Building upon this framework, we further propose an extended model, DC-CCNN++, to improve robustness under complex corruption conditions. Specifically, we introduce a Neuro-Inspired Robust Modulation-and-Readout Module (NRMR) to enhance feature stability and decision robustness through global-context gain modulation and dual-code evidence integration. We also design a Cortically Inspired Progressive Variability Training (CPVT) strategy, which progressively exposes the model to structured environmental variability while preserving stable clean-sample anchors during training. Experimental results show that DC-CCNN++ improves the performance of brain-inspired networks on point cloud analysis while maintaining performance comparable to state-of-the-art methods. Compared with the original DC-CCNN, it achieves stronger results on both classification and part segmentation, and exhibits enhanced robustness against sparsity, occlusion, Gaussian noise, salt-and-pepper noise, and spatial transformations. With its efficiency, robustness, and biologically grounded design, DC-CCNN++ provides a promising alternative to traditional deep learning methods for point cloud analysis. Code is available at https://anonymous.4open.science/r/DC-CCNNpp-44E3.

Problem

• point cloud analysis
• robustness
• energy efficiency
• corruption conditions
• sparsity

Innovation

• Brain-Inspired Neural Network
• Point Cloud Analysis
• Robustness
• Neuro-Inspired Modulation
• Progressive Variability Training