Embargo End Date2023-04-27
Permanent link to this recordhttp://hdl.handle.net/10754/676646
MetadataShow full item record
Access RestrictionsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2023-04-27.
AbstractDespite the significant progress in 3D vision in recent years, collecting large amounts of high-quality 3D data remains a challenge. Hence, developing solutions to extract 3D object information efficiently is a significant problem. We aim for an effective shape classification algorithm to facilitate accurate recognition and efficient search of sizeable 3D model databases. This thesis has two contributions in this space: a) a novel meta-learning approach for 3D object recognition and b) propose a new compositional 3D recognition task and dataset. For 3D recognition, we proposed a few-shot semi-supervised meta-learning model based on Pointnet++ representation with a prototypical random walk loss. In particular, we developed the random walk semi-supervised loss that enables fast learning from a few labeled examples by enforcing global consistency over the data manifold and magnetizing unlabeled points around their class prototypes. On the compositional recognition front, we create a large-scale, richly annotated stylized dataset called 3D CoMPaT. This large dataset primarily focuses on stylizing 3D shapes at part-level with compatible materials. We introduce Grounded CoMPaT Recognition as the task of collectively recognizing and grounding compositions of materials on parts of 3D Objects.
CitationLi, Y. (2022). Compositional and Low-shot Understanding of 3D Objects. KAUST Research Repository. https://doi.org/10.25781/KAUST-Y8162