Shape Prior Fusion for Efficient 3D Reconstruction

Creating accurate 3D models from images is a challenging problem in computer vision, especially for applications like autonomous driving, virtual reality, and mobile computing. Traditional Multi-View Stereo (MVS) methods rely on geometry to reconstruct shapes but often struggle with missing details due to occlusions or textureless surfaces. In this work, we introduce a learning-based approach that enhances MVS by integrating deep priors, allowing for more complete and accurate 3D reconstructions with fewer input images. Our method refines fine details using masked silhouette and depth map losses, ensuring better shape recovery. Tested on the ShapeNet dataset, our model achieves a Chamfer Distance of 0.018 and an IoU of 0.80, outperforming classical MVS while also improving surface coverage to 91.1% and density score to 85.7%. By combining deep learning with traditional geometry, our approach makes 3D reconstruction more reliable and efficient, opening new possibilities for real-world applications.