Reconstructing building interiors from images

By Yasutaka Furukawa, Brian Curless, S. M. Seitz, Richard Szeliski. 2009

375 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

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In Aerospace Engineering

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Citations in the corpus

RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials (2018, background): “As a thorough survey on 3D reconstruction techniques is beyond the scope of this paper, we discuss only the most related approaches and refer to [7, 13, 29] for a more thorough review.”
RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments (2012, background): “2007), and even unsorted collections of photos (Snavely et al. 2006; Furukawa et al. 2009).”
Tanks and temples (2017, methodology): “While to date use structure priors [Furukawa et al. 2009; Ikehata et al. 2015; Xiao and Furukawa 2014] or depth cameras [Choi et al. 2015; Zhou and Koltun 2013].”
Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction (2016, methodology): “Existing works on planar multi-view stereo or Manhattan world representations treat these as hard constraints [13, 14, 32].”
Omnidirectional 3D reconstruction in augmented Manhattan worlds (2014, background): “Note that this assumption does not require vertical planes to be orthogonal with respect to each other as in [6], [7], but only with respect to the”
A Survey of Urban Reconstruction (2013, methodology): “Several methods try to overcome this problem by including hierarchical models [LPK09], Manhattan-world assumptions [FCSS09a,FCSS09b], multi-layer depth maps [GPF10], or piece-wise planar priors [MK09,MK10, SSS09, CLP10, GFP10].”
RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments (2010, methodology): “One interesting line of work [7] attacks the harder problem of indoor reconstruction, using a Manhattanworld assumption to fit simple geometric models for visualization purposes.”
Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data (2011, background): “The geometry of as-is BIMs is starting to be generated using data from laser scanners, although image-based techniques show promise for some applications (Furukawa et al., 2009b).”
A Review of Heritage Building Information Modeling (H-BIM) (2018, methodology): “According to Grussenmeyer, 2008 [29] and Furukawa et al., 2009 [30], the post-processing of photographic reconstruction can be considered to be a great disadvantage.”
Towards Probabilistic Volumetric Reconstruction Using Ray Potentials (2015, background): “More recently, researchers have shifted their focus from reconstructing isolated single objects to more challenging general scenes that involve significant occlusions, textureless or reflective surfaces, transient objects, varying illumination conditions, and camera mis-registration errors [7, 16, 1”
Support Surface Prediction in Indoor Scenes (2013, result): “Finally, our work has some relation to efforts in 3D scene modeling (e.g., [2] [17] [13]).”
Multi-View Stereo: A Tutorial (2015, background): “This technique yields immersive visualization experiences, as the rendering is primarily based on real photographs and can convey even complex real photometric effects such as specular highlights or transparency/translucency, which are difficult to model [54, 68, 170].”
Robust reconstruction of indoor scenes (2015, background): “Many indoor reconstruction systems make simplifying assumptions and forfeit detail in the reconstructed model [19, 64, 7], rely on user interaction [17], or both [37, 53].”
Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization (2022, neutral): “Furukawa et al. [17] reconstruct ﬂoor plans from images using multi-view stereo combined with a Manhattan World assumption.”