Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction
We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU base...
| Main Authors: | , , , |
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| Format: | Journal article |
| Published: |
Springer Verlag
2019
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