Sketch-based shape retrieval

354 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Multi-view Convolutional Neural Networks for 3D Shape Recognition (2015, methodology): “[12] compared human sketches with line drawings of 3D models produced from several different views based on local Gabor filters, while Schneider et al.”
Sketch-based 3D shape retrieval using Convolutional Neural Networks (2015, methodology): “Eitz et al. [9] collected sketches based on the PSB dataset.”
How do humans sketch objects? (2012, background): “Retrieval results are purely based on geometric similarity between the sketch and the image content [Chalechale et al. 2005; Eitz et al. 2011a; Shrivastava et al. 2011; Eitz et al. 2012].”
Deep Learning for Free-Hand Sketch: A Survey (2020, background): “More specifically, coarse-grained uni-modal datasets [5], [71] support sketch recognition and retrieval; while coarsegrainedmulti-modal datasets (e.”
The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs (2015, background): “It is the most effective feature in the context of object detection [21] and also the most favored local feature in the context of sketch-based retrieval [44, 16, 17].”
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching (2016, methodology): “This problem has recently drawn the attention of the machine learning community as a fertile playground for crossmodal feature learning [7, 9, 14, 30, 11].”