3D Reconstruction of Shoes for Augmented Reality
This addresses the limitations of static 2D images for online shoe shoppers by providing more realistic virtual interactions, with potential applicability to broader fashion categories.
This paper tackles the problem of enhancing online shoe shopping by developing a mobile-based solution that generates realistic 3D shoe models from 2D images, achieving an average PSNR of 32 and enabling immersive AR interactions via smartphones.
This paper introduces a mobile-based solution that enhances online shoe shopping through 3D modeling and Augmented Reality (AR), leveraging the efficiency of 3D Gaussian Splatting. Addressing the limitations of static 2D images, the framework generates realistic 3D shoe models from 2D images, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 32, and enables immersive AR interactions via smartphones. A custom shoe segmentation dataset of 3120 images was created, with the best-performing segmentation model achieving an Intersection over Union (IoU) score of 0.95. This paper demonstrates the potential of 3D modeling and AR to revolutionize online shopping by offering realistic virtual interactions, with applicability across broader fashion categories.