Feature point detection on textureless surfaces remains a fundamental challenge in computer vision due to the absence of discernible color and brightness gradients. From the imaging mechanism perspective, micro-geometry structures of textureless surfaces provide physically stable cues for feature point extraction despite the absence of visual distinctiveness. Therefore, we propose a novel feature point detection method, which reconstructs surface micro-geometry structures from a single RGB image and leverages these micro-geometry structures for feature extraction, without relying on specialized equipment or complex deep learning models. Specifically, our method establishes a novel framework that models the light-surface interaction to analyze phase modulation in reflected light. Then it reconstructs underlying micro-geometry structures through Gabor Kernel-based spectral analysis, enabling accurate quantification of surface height variations from phase information. This information forms the foundation of our proposed Concave-Convex Index (CCI), a robust geometric descriptor that achieves stable feature characterization through geometry-aware measurements. Extensive evaluations on TUM, T-LESS, Shape2.5D datasets and self-collected images, demonstrate our method's superior capability in extracting stably distributed and highly repeatable feature points, even when visible texture or brightness gradients vanish. Our method offers a novel perspective for reliable feature point detection on challenging textureless surfaces across diverse materials and illumination conditions.

Surface normal estimation is a fundamental task in point cloud processing and plays a crucial role in downstream applications. Existing methods typically extract features from local neighborhoods or patches, followed by surface fitting or direct regression to predict normals. However, the scale ambiguity in determining the optimal neighborhood hinders effective extraction of geometric information, making normal estimation for unstructured point clouds with significant density variations particularly challenging. To address this challenge, we propose APN-Net, an adaptive perception network for point cloud normal estimation. Specifically, we design the Graphical Information Self-perception (GIS) module, which provides an implicit manner for region partitioning and expands the receptive field, enabling automatic extraction of both local geometric details and global structural information, while alleviating the scale ambiguity in determining the optimal neighborhood. Moreover, to capture complex geometric details, we introduce the Adaptive Graph Convolution (AGC) module, which employs adaptive kernels to model relationships among points across different semantic regions, thereby enabling richer feature representation. Extensive experiments on both synthetic and real-world scanned datasets demonstrate that APN-Net achieves superior performance in unoriented normal estimation, particularly for point clouds with significant density variations.