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OperatorNet: Recovering 3D Shapes From Difference Operators., , , , and . ICCV, page 8587-8596. IEEE, (2019)Scene Structure Inference through Scene Map Estimation., , , and . VMV, Eurographics Association, (2016)SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence., , and . 3DV, page 144-154. IEEE, (2022)Affection: Learning Affective Explanations for Real-World Visual Data., , , and . CVPR, page 6641-6651. IEEE, (2023)Generalizable Local Feature Pre-training for Deformable Shape Analysis., , and . CVPR, page 13650-13661. IEEE, (2023)Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence., , and . CVPR, page 8589-8598. Computer Vision Foundation / IEEE, (2020)CrossLink: joint understanding of image and 3D model collections through shape and camera pose variations., , and . ACM Trans. Graph., 34 (6): 233:1-233:13 (2015)Topic Modeling for Personalized Recommendation of Volatile Items., and . ECML/PKDD (2), volume 6322 of Lecture Notes in Computer Science, page 483-498. Springer, (2010)Discrete Time Evolution Process Descriptor for Shape Analysis and Matching., , , , and . ACM Trans. Graph., 37 (1): 4 (2018)Implicit Field Supervision for Robust Non-rigid Shape Matching., , and . ECCV (3), volume 13663 of Lecture Notes in Computer Science, page 344-362. Springer, (2022)