• Introduces a novel single-image 3D mesh reconstruction method that leverages depth consistency without requiring viewpoint pose annotations, overcoming limitations of silhouette-based supervision.
• Employs standard deviation and Laplacian losses to regulate mesh edge distribution, leading to more precise reconstructions with finer structural details.
• Demonstrates superior performance on both synthetic and real-world datasets, outperforming existing view-based 3D reconstruction methods.
• Provides a practical solution for applications in robotics, autonomous driving, and 3D animation by enabling accurate 3D shape inference from a single perspective.