UCLID-Net: Single View Reconstruction in Object Space
This work addresses the challenge of reconstructing 3D objects from single images for computer vision applications, offering an incremental improvement over existing methods.
The paper tackles the problem of single-view 3D reconstruction by proposing a geometry-preserving latent space that preserves Euclidean structure, which boosts performance and outperforms state-of-the-art methods on both synthetic ShapeNet images and real-world images.
Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations. However, these representations rarely preserve the Euclidean structure of the 3D space objects exist in. In this paper, we show that building a geometry preserving 3-dimensional latent space helps the network concurrently learn global shape regularities and local reasoning in the object coordinate space and, as a result, boosts performance. We demonstrate both on ShapeNet synthetic images, which are often used for benchmarking purposes, and on real-world images that our approach outperforms state-of-the-art ones. Furthermore, the single-view pipeline naturally extends to multi-view reconstruction, which we also show.