Gmd: Gaussian mixture descriptor for pair matching of 3D fragments

In the automatic reassembly of fragments acquired using laser scanners to reconstruct objects, a crucial step is the matching of fractured surfaces. In this paper, we propose a novel local descriptor that uses the Gaussian Mixture Model (GMM) to fit the distribution of points, allowing for the description and matching of fractured surfaces of fragments. Our method involves dividing a local surface patch into concave and convex regions for estimating the k value of GMM. Then the final Gaussian Mixture Descriptor (GMD) of the fractured surface is formed by merging the regional GMDs. To measure the similarities between GMDs for determining adjacent fragments, we employ the L2 distance and align the fragments using Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP). The extensive experiments on real-scanned public datasets and Terracotta datasets demonstrate the effectiveness of our approach; furthermore, the comparisons with several existing methods also validate the advantage of the proposed method.

mmMirror: Device Free mmWave Indoor NLoS Localization Using Van-Atta-Array IRS

Industry 4.0 is transforming manufacturing and logistics by integrating robots into shared human environments, such as factories, warehouses, and healthcare facilities. However, the risk of human-robot collisions, especially in Non-Line-of-Sight (NLoS) scenarios like around corners, remains a critical challenge. Existing solutions, such as vision-based and LiDAR systems, often fail under occlusion, lighting constraints, or privacy concerns, while RF-based systems are limited by range and accuracy. To address these limitations, we propose mmMirror, a novel system leveraging a Van Atta Array-based millimeter-wave (mmWave) reconfigurable intelligent reflecting surface (IRS) for precise, device-free NLoS localization. mmMirror integrates seamlessly with existing frequency-modulated continuous-wave (FMCW) radars and offers: (i) robust NLoS localization with centimeter-level accuracy at ranges up to 3 m, (ii) seamless uplink and downlink communication between radar and IRS, (iii) support for multi-radar and multi-target scenarios via dynamic beam steering, and (iv) reduced scanning latency through adaptive time slot allocation. Implemented using commodity 24 GHz radars and a PCB-based IRS prototype, mmMirror demonstrates its potential in enabling safe human-robot interactions in dynamic and complex environments.

N$^2$LoS: Single-Tag mmWave Backscatter for Robust Non-Line-of-Sight Localization

The accuracy of traditional localization methods significantly degrades when the direct path between the wireless transmitter and the target is blocked or non-penetrable. This paper proposes N2LoS, a novel approach for precise non-line-of-sight (NLoS) localization using a single mmWave radar and a backscatter tag. N2LoS leverages multipath reflections from both the tag and surrounding reflectors to accurately estimate the targets position. N2LoS introduces several key innovations. First, we design HFD (Hybrid Frequency-Hopping and Direct Sequence Spread Spectrum) to detect and differentiate reflectors from the target. Second, we enhance signal-to-noise ratio (SNR) by exploiting the correlation properties of the designed signals, improving detection robustness in complex environments. Third, we propose FS-MUSIC (Frequency-Spatial Multiple Signal Classification), a super resolution algorithm that extends the traditional MUSIC method by constructing a higher-rank signal matrix, enabling the resolution of additional multipath components. We evaluate N2LoS using a 24 GHz mmWave radar with 250 MHz bandwidth in three diverse environments: a laboratory, an office, and an around-the-corner corridor. Experimental results demonstrate that N2LoS achieves median localization errors of 10.69 cm (X) and 11.98 cm (Y) at a 5 m range in the laboratory setting, showcasing its effectiveness for real-world NLoS localization.