Learning Category-Specific Mesh Reconstruction from Image Collections
This addresses 3D reconstruction from images for computer vision applications, but it is incremental as it builds on existing deformable model approaches.
The paper tackles the problem of reconstructing 3D shape, camera, and texture from a single image using a deformable mesh model, achieving results on CUB and PASCAL3D datasets without ground-truth 3D supervision.
We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image. The shape is represented as a deformable 3D mesh model of an object category where a shape is parameterized by a learned mean shape and per-instance predicted deformation. Our approach allows leveraging an annotated image collection for training, where the deformable model and the 3D prediction mechanism are learned without relying on ground-truth 3D or multi-view supervision. Our representation enables us to go beyond existing 3D prediction approaches by incorporating texture inference as prediction of an image in a canonical appearance space. Additionally, we show that semantic keypoints can be easily associated with the predicted shapes. We present qualitative and quantitative results of our approach on CUB and PASCAL3D datasets and show that we can learn to predict diverse shapes and textures across objects using only annotated image collections. The project website can be found at https://akanazawa.github.io/cmr/.