Learning 3D Shape and Possibilistic Representations from Unstructured Images

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Computer Vision

Learning 3D Shape and Possibilistic Representations from Unstructured Images

Shelf-Supervised Mesh Prediction in the Wild

We propose a learning-based approach that can infer 3-d shape and pose of object from a single image and propose a learning-based approach that can train from unstructured image collections, supervised by only segmentation outputs from off-the-shelf recognition systems(i.e.

We first infer a volumetric representation in a canonical frame, along with the camera pose.

The coarse volumetric prediction is then converted to a mesh-based representation, which is further refined in the predicted camera frame.

These two steps allow both shape-pose factorization from image collections and per-instancereconstruction in finer details.

We enforce the representationgeometrically consistent with both appearance and masks, and also that the synthetic novel views are indistinguishable from image collections.

We examine the method on both synthetic and real-world datasets and demonstrate its scalability on 50 categories in the wild, an order of magnitude more classes than existing works.

Authors

Yufei Ye, Shubham Tulsiani, Abhinav Gupta

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