Abstract: In deep learning-based multi-view stereo(MVS) reconstruction,cost volume construction faces challenges of high computational complexity and memory consumption.Existing studies often employ cascade architectures or iterative optimization methods to reduce memory usage.However,the coarse-to-fine sampling strategy in cascade structures may lead to the loss of fine-grained details,weakening the perception of critical features.To address this,this paper proposes a novel multi-view stereo network framework based on a cascade structure with binary search and multi-granularity feature aggregation.The proposed framework reduces memory overhead through a cascade architecture while employing a binary search strategy to partition the depth range into multiple candidate regions.A discrete classification method is introduced to compress the depth search space,improving depth retrieval efficiency and lowering memory requirements.Furthermore,this paper proposes a multi-granularity feature aggregation strategy that embeds coarse-grained global semantic information into fine-grained cost volume construction while preserving attention to fine-grained local texture details.By fusing multi-level feature representations and incorporating intra-view adaptive aggregation and view-wise adaptive weighting strategies in the aggregation module,the proposed model enhances the perception of both global structures and local detailed features.Experimental results on the DTU and Tanks & Temples benchmark datasets demonstrate that the proposed method achieves superior point cloud reconstruction quality while maintaining low memory consumption.

Key words: Multi-view stereo, Binary search strategy, Multi-granularity feature aggregation