Homography VAE: Automatic Bird's Eye View Image Reconstruction from Multi-Perspective Views

Keisuke Toida, Naoki Kato, Osamu Segawa, Takeshi Nakamura, Kazuhiro Hotta

Abstract: We propose Homography VAE, a novel architecture that combines Variational AutoEncoders with Homog-raphy transformation for unsupervised standardized view image reconstruction. By incorporating coordinate transformation into the VAE framework, our model decomposes the latent space into feature and transformation components, enabling the generation of consistent standardized view from multi-viewpoint images without explicit supervision. Effectiveness of our approach is demonstrated through experiments on MNIST and GRID datasets, where standardized reconstructions show significantly improved consistency across all evaluation metrics. For the MNIST dataset, the cosine similarity among standardized view achieved 0.66, while original and transformed views show 0.29 and 0.37, respectively. The number of PCA components required to explain 95% of the variance decreases from 193.5 to 33.2, indicating more consistent representations. Even more pronounced improvements are observed on GRID datase