Omnidirectional 3D reconstruction in augmented Manhattan worlds

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A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “own in Figure 3. Some distortions inevitably remain, but despite these challenges in calibration, omnidirectional cameras have been used for many applications such as SLAM [143] and 3D reconstruction [144]. FIGURE 8: An urban scene near Nagoya University, with camera and lidar data collected by our ”
Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels (2017, background): “Existing works that consider low-level geometric priors such as spatial smoothness [6, 22, 38], piecewise planarity [16, 33] or Manhattan-world constraints [12, 31] cannot complete large occluded regions, especially for objects with complex geometry.”
Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction (2016, methodology): “Reconstruction with Primitives: Several methods exploit planar patches to represent piece-wise planar [3, 6, 10, 12, 34,41,45,46,56] or Manhattan world [13,14,42,47] scenes.”
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer (2015, background): “Compared to omnidirectional camera systems [36, 37] our setup benefits from increased resolution.”
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D (2021, background): “Compared to omnidirectional camera systems [90], [91], our setup beneﬁts from increased resolution of the 3D reconstruction.”