  View based strategies for 3D object recognition have proven to be very
successful. The state-of-the-art methods now achieve over 90% correct category
level recognition performance on appearance images. We improve upon these
methods by introducing a view clustering and pooling layer based on dominant
sets. The key idea is to pool information from views which are similar and thus
belong to the same cluster. The pooled feature vectors are then fed as inputs
to the same layer, in a recurrent fashion. This recurrent clustering and
pooling module, when inserted in an off-the-shelf pretrained CNN, boosts
performance for multi-view 3D object recognition, achieving a new state of the
art test set recognition accuracy of 93.8% on the ModelNet 40 database. We also
explore a fast approximate learning strategy for our cluster-pooling CNN,
which, while sacrificing end-to-end learning, greatly improves its training
efficiency with only a slight reduction of recognition accuracy to 93.3%. Our
implementation is available at https://github.com/fate3439/dscnn.
