SHREC’16 Track Large-Scale 3D Shape Retrieval from ShapeNet Core55

By M. Savva, F. Yu, Hao Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, H. Su, S. Bai, X. Bai and 11 more. 2016

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2016, methodology): “The architecture of our network (Sec 4.2) is inspired by the properties of point sets in R n (Sec 4.1).”
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network (2018, methodology): “…CM [34] 12.59 MLP/Sorted CM [34] 16.06 Spherical CNN [1] 8.47 Ours (FFS2CNN) 7.97 3D Shape Recognition Finally, we report results for shape classiﬁcation using the SHREC17 dataset [36], which is a subset of the larger ShapeNet dataset [37] having roughly 51300 3D models spread over 55 categories.”
Spherical CNNs (2018, methodology): “The SHREC17 task (Savva et al., 2017) contains 51300 3D models taken from the ShapeNet dataset (Chang et al., 2015) which have to be classiﬁed into 55 common categories (tables, airplanes, persons, etc.).”
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018, background): “Another related research direction is retrieving similar 3D shapes given a single image, instead of reconstructing the object’s actual geometry [1, 15, 19, 48].”
Triplet-Center Loss for Multi-view 3D Object Retrieval (2018, methodology): “Especially LWBR gets 75.2% mAP on SHREC’13, nearly 8% higher than DCML.”
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition (2019, methodology): “We conducted experiments on three different benchmark datasets ranging from ModelNet40 classification [27], SHREC’17 perturbed retrieval [19] and ShapeNet part segmentation [29].”
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network (2018, methodology): “Finally, the performance of our Point2Sequence is evaluated in the part segmentation task on ShapeNet part dataset (Savva et al. 2016).”
Deep Learning for 3D Point Clouds: A Survey (2019, methodology): “The inference time on the ModelNet and ShapeNet datasets is sped up by 1.5 ms and 32 times over PointNet on a moderate machine.”
3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation (2019, methodology): “To employ the content information within all views, Li et al. [24] concatenated all content information for hierarchical abstraction in the CNN-based model.”