Boundary Cues for 3D Object Shape Recovery

By Kevin Karsch, Zicheng Liao, Jason Rock, Jonathan T. Barron, Derek Hoiem. 2013

31 citations recorded by the source corpus.

Early work in computer vision considered a host of geometric cues for both shape reconstruction and recognition. However, since then, the vision community has focused heavily on shading cues for reconstruction, and moved towards data-driven approaches for recognition. In this paper, we reconsider these perhaps overlooked "boundary" cues (such as self occlusions and folds in a surface), as well as many other established constraints for shape reconstruction. In a variety of user studies and quantitative tasks, we evaluate how well these cues inform shape reconstruction (relative to each other) in terms of both shape quality and shape recognition. Our findings suggest many new directions for future research in shape reconstruction, such as automatic boundary cue detection and relaxing assumptions in shape from shading (e.g. orthographic projection, Lambertian surfaces).

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geology

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Citations in the corpus

Lifting Object Detection Datasets into 3D (2015, methodology): “They can be built from 3D scans of different instances of the class, e.g. the face model in [31] and the human body model in [32], or using 3D meshes obtained from shape repositories, such as Google Sketchup as in [33].”
Unfolding an Indoor Origami World (2014, methodology): “These have been applied in the context of stereo [37], but have largely been ignored in the single-image layout community, apart from work on shape recovery using hand-marked folds [19].”
Designing deep networks for surface normal estimation (2014, background): “Another theme that has emerged both in the past [33, 17, 2, 19] and recently [16, 20, 10] is the reasoning between surface normals and the edges in the images.”
Reconstructing PASCAL VOC (2014, methodology): “Recently and perhaps closest to our approach, Karsch et al.[17] experimented with reconstructing VOC objects, using manual curvature annotations on boundaries but computed 2.5D reconstructions while we focus on the full 3D problem.”
Learning Category-Specific Deformable 3D Models for Object Reconstruction (2017, background): “Index Terms—Object reconstruction, 3D shape modeling, viewpoint estimation, scene understanding Ç”