Efficiency is crucial for primitive segmentation in industrial applications. Previous state-of-the-art (SOTA) methods suffer from low efficiency due to reliance on time-consuming feature clustering. To circumvent the need for slowly grouping points in a high-dimensional space, we propose a fast framework named PrimitiveGroup (PG), which makes full use of spatial relations and various feature consistency to group points efficiently in the 3-D space. Moreover, to improve the accuracy of point grouping in the 3-D space, we also introduce an adaptive long-range offset prediction module which expands the neighborhood perception range and adaptively focuses on those neighborhoods exhibiting higher semantic and instance correlation. A hybrid consistency aggregation that considers not only spatial distance and semantic constraints but also other geometric consistency of each point is proposed to decompose mixed points belonging to primitives with overlapping or adjacent centroids. Experimental results on the ABCParts and the ANSI datasets show that PG not only achieves competitive performance compared to recent SOTA methods but also operates as the fastest deep learning method in primitive segmentation, 22 times faster than the existing fastest method. Meanwhile, PG achieves promising robustness on noisy point clouds and industrial real scans.

Spiking neural networks (SNNs), inspired by the inherent spiking computation paradigm of the biological neural systems, have exhibited superior energy efficiency in 2D classification tasks over traditional artificial neural networks (ANNs). However, the regression potential of SNNs has not been well explored, especially in 3D point cloud processing. In this paper, we propose noise-injected spiking graph convolutional networks to leverage the full regression potential of SNNs in 3D point cloud denoising. Specifically, we first emulate the noise-injected neuronal dynamics to build noise-injected spiking neurons. On this basis, we design noise-injected spiking graph convolution for promoting disturbance-aware spiking representation learning on 3D points. Starting from the spiking graph convolution, we build two SNN-based denoising networks. One is a purely spiking graph convolutional network, which achieves low accuracy loss compared with some ANN-based alternatives, while resulting in significantly reduced energy consumption on two benchmark datasets, PU-Net and PC-Net. The other is a hybrid architecture, which integrates some ANN-based learning operations and exhibits a high performance-efficiency trade-off with only a few time steps. Our work lights up SNN’s potential for 3D point cloud denoising, injecting new perspectives of exploring the deployment on neuromorphic chips while paving the way for developing energy-efficient 3D data acquisition devices.

Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Traditional geometric methods and recent learning-based approaches frequently struggle with these structures, as both rely heavily on sufficient contextual information from local point neighborhoods. However, 3D measurement data of thin-walled structures often lack the accurate, dense, and regular neighborhood sampling required for reliable edge extraction, resulting in degraded performance.In this work, we introduce STAR-Edge, a novel approach designed for detecting and refining edge points in thin-walled structures. Our method leverages a unique representation—the local spherical curve—to create structure-aware neighborhoods that emphasize co-planar points while reducing interference from close-by, non-co-planar surfaces. This representation is transformed into a rotation-invariant descriptor, which, combined with a lightweight multi-layer perceptron, enables robust edge point classification even in the presence of noise and sparse or irregular sampling. Besides, we also use the local spherical curve representation to estimate more precise normals and introduce an optimization function to project initially identified edge points exactly on the true edges. Experiments conducted on the ABC dataset and thin-walled structure-specific datasets demonstrate that STAR-Edge outperforms existing edge detection methods, showcasing better robustness under various challenging conditions. The source code is available at https://github.com/miraclelzk/star-edge.

Ensuring the safety and extended operational life of fighter aircraft necessitates frequent and exhaustive inspections. While surface defect detection is feasible for human inspectors, manual methods face critical limitations in scalability, efficiency, and consistency due to the vast surface area, structural complexity, and operational demands of aircraft maintenance. We propose a smart surface damage detection and localization system for fighter aircraft, termed J-DDL. J-DDL integrates 2D images and 3D point clouds of the entire aircraft surface, captured using a combined system of laser scanners and cameras, to achieve precise damage detection and localization. Central to our system is a novel damage detection network built on the YOLO architecture, specifically optimized for identifying surface defects in 2D aircraft images. After detecting damage in 2D images, the system maps the identified anomalies onto corresponding 3D point clouds, enabling accurate 3D localization of defects across the aircraft surface. To facilitate further advancements in this domain, we have developed the first publicly available dataset specifically focused on aircraft damage. Experimental evaluations validate the effectiveness of our framework, underscoring its potential to significantly advance automated aircraft inspection technologies.

Abstract Spherical harmonics are a favorable technique for 3D representation, employing a frequency‐based approach through the spherical harmonic transform (SHT). Typically, SHT is performed using equiangular sampling grids. However, these grids are non‐uniform on spherical surfaces and exhibit local anisotropy, a common limitation in existing spherical harmonic decomposition methods. This paper proposes a 3D representation method using Fibonacci Spherical Harmonics (FSH3D). We introduce a spherical Fibonacci grid (SFG), which is more uniform than equiangular grids for SHT in the frequency domain. Our method employs analytical weights for SHT on SFG, effectively assigning sampling errors to spherical harmonic degrees higher than the recovered band‐limited function. This provides a novel solution for spherical harmonic transformation on non‐equiangular grids. The key advantages of our FSH3D method include: 1) With the same number of sampling points, SFG captures more features without bias compared to equiangular grids; 2) The root mean square error of 32‐degree spherical harmonic coefficients is reduced by approximately 34.6% for SFG compared to equiangular grids; and 3) FSH3D offers more stable frequency domain representations, especially for rotating functions. FSH3D enhances the stability of frequency domain representations under rotational transformations. Its application in 3D shape reconstruction and 3D shape classification results in more accurate and robust representations. Our code is publicly available at https://github.com/Miraclelzk/Fibonacci-Spherical-Harmonics .

Fast and accurate registration of outdoor LiDAR point clouds poses a considerable challenge for their large-scale (e.g., 300 K points) and intricate (e.g., noise and outliers) distributions. In this article, we present a fast low-frequency coarse registration method for large-scale outdoor LiDAR point clouds, dubbed FlyCore. Different from existing methods, FlyCore is very fast for practical applications and bridges current refinement registration methods smoothly for their accuracy improvements. Specifically, we first construct spherical feature spaces for a pair of point clouds based on their keypoints and saliency uncertainties independently. Then, we perform harmonic decomposition on these spherical feature spaces, utilizing the low-frequency components of spherical harmonics (SHs) to implement point cloud registration. FlyCore demonstrates less sensitivity to noise and outliers compared to feature-based registration techniques. Also, FlyCore achieves exceptionally low time complexity by eliminating the need for feature matching and iterative procedures, ensuring fine alignment with only a few iterations. Experimental validations, utilizing two extensive LiDAR datasets featuring urban and natural scenarios, confirm the effectiveness and accuracy improvement of existing fine registration methods facilitated by our FlyCore.