Efficient Globally Optimal 2D-to-3D Deformable Shape Matching

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Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling (2018, background): “While these datasets have helped to advance the field of 3D shape modeling, they have their respective limita- tions: datasets like ShapeNet or Elastic2D3D [32] do not have real images, and recent 3D reconstruction challenges using ShapeNet have to be exclusively on synthetic images [67]; Pascal 3D+”
Dynamic FAUST: Registering Human Bodies in Motion (2017, unknown): “From this dataset, researchers have used both real scans (for benchmarking registration techniques [20, 51, 55]) and aligned templates (e.g. for training Convolutional Neural Networks [9, 10, 11, 33, 53]).”
Product Manifold Filter: Non-rigid Shape Correspondence via Kernel Density Estimation in the Product Space (2017, unknown): “Windheuser et al. [43] seek for a two-dimensional minimal surface in the four-dimensional product space of the two input surfaces; this was later extended to a 2D-to-3D setting by Lähner et al. [24].”
ObjectNet3D: A Large Scale Database for 3D Object Recognition (2016, background): “[17] introduces a new dataset for 2D-3D deformable shape matching.”