Class Specific 3D Object Shape Priors Using Surface Normals

By Christian Häne, Nikolay Savinov, Marc Pollefeys. 2014

57 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning a Multi-View Stereo Machine (2017, methodology): “Learning has been used for multi-view reconstruction in the form of shape priors for objects [2, 9, 58, 20, 27, 52], or semantic class specific surface priors for scenes [22, 17, 45].”
Hierarchical Surface Prediction for 3D Object Reconstruction (2017, background): “For such cases object shape priors have been proposed [8], [14], [36].”
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction (2016, methodology): “Thus, shape prior-based methods can work with fewer images and with fewer assumptions on the object reflectance function as shown in [16, 6, 25].”
Deep Marching Cubes: Learning Explicit Surface Representations (2018, background): “Unfortunately, the expressiveness of such prior models is limited to simple local smoothness assumptions [2, 17, 21, 47] or very specialized shape models [1, 15, 16, 42].”
Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction (2016, background): “, by constraining the set of plausible geometries [2, 11, 19] or by modeling class specific normal distributions [20,21].”
Mesh R-CNN (2019, background): “Multi-View Shape Prediction There is a broad line of work on multi-view reconstruction of objects and scenes, from classical binocular stereo [17, 53] to using shape priors [1, 2, 6, 21] and modern learning techniques [24, 26, 54].”
Segment based 3D object shape priors (2015, methodology): “Convex multi-label segmentation has been used in dense volumetric reconstruction to jointly infer semantic labels and geometry [10] and to define per object shape priors for challenging classes [9].”
Dense Semantic 3D Reconstruction (2017, methodology): “The method in [12] was inspired by the results we got with the method presented in this paper and was”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “Monocular Video: Failures in multi-view stereo cause problems for approaches like Haene et al. (2013) which require dense depth measurements.”