3D Reconstruction of Shoes for Augmented Reality
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 29 Jan 2025.
Builds on
8 of 13 references.
References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2022Detecting Twenty-thousand Classes using Image-level Supervisioncited by 613
- 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 611
- 2018nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentationcited by 291
- 2021ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphonescited by 2
Built on by
Not cited yet.