The sharp, intricate contours of lunar regolith particles hold critical clues to the Moon's geological evolution and inform engineering applications from habitat construction to spacecraft design, making their precise segmentation a task of significant scientific and engineering value. However, this task exposes a weakness in deep learning models known as spectral bias, an inherent tendency to learn smooth, low-frequency functions which causes them to systematically erase the very high-frequency boundary details that are of primary interest. To resolve this conflict, we propose a framework to deeply seek object boundaries. First, we propose High-Frequency Initialized LoRA (HiFi-LoRA) to counteract spectral bias. By initializing the LoRA adaptation matrices as the optimal low-rank approximation of a high-pass filter, it fundamentally enhances the model's high-frequency perception and injects a strong preference for edges. Second, we propose the Wavelet Energy Modulation (WEM) regularizer. It guides the model to learn the intrinsic correlation between contour complexity and mask area, forcing the model to build a geometric understanding of contour morphology upon its high-frequency perception, thereby enabling the generation of boundary details commensurate with the object's scale. Experimentally, we constructed the Lunar Regolith Segmentation Dataset (LRSD), the first large-scale benchmark with expert-annotated contours. Extensive experiments demonstrate that our method sets a new state of the art on this challenging benchmark, not only achieving top performance on regional metrics like mIoU and DSC but, more critically, drastically outperforming existing models on boundary accuracy. This work not only provides a powerful computational tool for lunar science but also offers a robust and synergistic design pattern for other fine-grained segmentation challenges.

The authors present a local stereo matching algorithm whose performance is insensitive to changes in radiometric conditions between the input images. First, a prior on the disparities is built by combining the DAISY descriptor and Census filtering. Then, a Census‐based cost aggregation with a self‐adaptive window is performed. Finally, the maximum a‐posteriori estimation is carried out to compute the disparity. The authors’ algorithm is compared with both local and global stereo matching algorithms (NLCA, ELAS, ANCC, AdaptWeight and CSBP) by using Middlebury datasets. The results show that the proposed algorithm achieves high‐accuracy dense disparity estimations and is more robust to radiometric differences between input images than other algorithms.