A Lightweight 3D Object Feature Recognition Method Based on Camera Displacement and Pixel Change Analysis

Abstract To address the bottlenecks of existing 3D perception technologies, such as high algorithm complexity, exorbitant hardware costs, and insufficient real-time performance, this paper innovatively proposes a lightweight 3D object property recognition method based on camera displacement. By employing a time-sliced focusing strategy and analyzing the objective laws of pixel changes during continuous camera displacement, combined with a dynamic region segmentation strategy along the vertical plumb line, the method achieves rapid identification and 3D modeling of high-convex, low-concave, and planar objects. Its core advantage lies in sharing camera data sources and computational cores with deep learning methods while supporting distributed collaborative operations. Unlike traditional deep learning-based approaches, the proposed method does not require pre-training of object attributes; instead, it determines 3D properties solely through the increasing, decreasing, or offset patterns