  3D shape captioning is a challenging application in 3D shape understanding.
Captions from recent multi-view based methods reveal that they cannot capture
part-level characteristics of 3D shapes. This leads to a lack of detailed
part-level description in captions, which human tend to focus on. To resolve
this issue, we propose ShapeCaptioner, a generative caption network, to perform
3D shape captioning from semantic parts detected in multiple views. Our novelty
lies in learning the knowledge of part detection in multiple views from 3D
shape segmentations and transferring this knowledge to facilitate learning the
mapping from 3D shapes to sentences. Specifically, ShapeCaptioner aggregates
the parts detected in multiple colored views using our novel part class
specific aggregation to represent a 3D shape, and then, employs a sequence to
sequence model to generate the caption. Our outperforming results show that
ShapeCaptioner can learn 3D shape features with more detailed part
characteristics to facilitate better 3D shape captioning than previous work.
