Core Viewpoint - The article discusses the SERES (Semantic-Aware Reconstruction from Sparse Views) method, which addresses the challenges of geometric accuracy, detail restoration, and structural integrity in 3D reconstruction from sparse views, providing a low-cost solution to enhance clarity and completeness of geometry [4][27]. Summary by Sections Introduction to SERES - SERES is developed by a collaborative team from Shanghai Jiao Tong University, the University of Manchester, and the Chinese University of Hong Kong, and has been accepted by IEEE Transactions on Visualization and Computer Graphics [6]. Method Overview - The SERES design focuses on two main lines: semantic matching priors and region-level regularization, integrating these into existing frameworks like NeuS or Neuralangelo without altering the core rendering and implicit surface expressions [8]. Semantic Matching Priors - The method involves extracting stable semantic blocks and geometric primitives from input images, allowing for interactive alignment and aggregation across multiple views, which helps the model recognize corresponding details during training [10][12]. Region-Level Regularization - SERES introduces interpretable region consistency in image space, aligning segmented regions with the model's rendered semantic distribution, which provides strong signals for how shapes should align, effectively reducing noise and improving surface coherence [14][22]. Experimental Results - In sparse multi-view settings, SERES significantly improves reconstruction quality and new view synthesis quality, showing a consistent decrease in geometric error as the number of views increases, indicating stable benefits across varying sparsity levels [17][18]. Conclusion - SERES transforms cross-view semantic consistency and structural region constraints into a low-cost, interpretable, and reusable training prior, making it suitable for integration into current sparse 3D reconstruction workflows, achieving high-fidelity geometry with fewer views [27].