Fig. 1. Camera model with definition of coordinate systems

Fig. 2. Multiview geometry model. (a) Multiview imaging with stereo correspondences; (b) space resection among backward projection rays

Fig. 3. Structure from motion problem ^[29]

Fig. 4. Integral photography proposed by Lippmann^[17]. (a) Basic imaging principle; (b) light field sampling mode

Fig. 5. 3D integral imaging based on axially distributed sensing ^[53]. (a) Pickup strategy for elemental images; (b) reconstructed images at different depths

Fig. 6. 3D integral imaging based on off-axially distributed sensing^[56]. (a) Pickup strategy for elemental images; (b) two elemental images with object occlusion and 3D reconstructed slice images

Fig. 7. 3D integral imaging based on planar camera motion^[59]. (a) Pickup strategy for elemental images; (b) reconstructed image for “car” object; (c) reconstructed image for “signal” object

Fig. 8. 3D imaging system structure with a set of planar mirrors. (a) One mirror ^[62]; (b) two mirrors ^[64]; (c) three mirrors ^[66]; (d) four mirrors ^[68]

Fig. 9. 3D profile and deformation measurement method combining four-mirror-based 3D imaging system with digital image correlation technique ^[72]

Fig. 10. Dynamic 3D imaging method combining four-mirror catadioptric system with a pan-tilt mirror ^[74]. (a) Optical arrangement; (b) 3D tracking and imaging for a dynamic object

Fig. 11. 3D imaging methods using curved mirrors. (a)-(c) System with two oppositely configured hyperboloidal mirrors, panoramic image captured by system, and 3D reconstruction results^[77]; (d)-(e) system with an array of spherical mirrors, and 3D reconstructed images^[79]

Fig. 12. 3D stereo imaging technique using a biprism. (a)-(c) 3D measurement principle based on modified virtual point model, system structure and measurement error map ^[82]; (d)-(f) 3D reconstruction principle based on perspective projection model, system structure and reconstructed object ^[83]

Fig. 13. Stereo endoscopic imaging method using microprism arrays^[89]. (a) System setup; (b) 3D imaging model; (c) prototype of microprism array and system; (d) reconstructed depth maps for an object at different distances

Fig. 14. 3D imaging with an optically transparent plate. (a)-(c) System setup, depth estimation maps, and 3D reconstructed images^[92]; (c)-(f) system setup with a MEMS-driven plate, rectified image, and depth map^[93]

Fig. 15. 3D imaging using a rotational wedge prism^[95]. (a) System structure; (b) imaging model; (c) multiview stereo image matching; (d) 3D profile reconstruction; (e) 3D scale reconstruction

Fig. 16. 3D information acquisition based on diffraction grating. (a) 3D digital image correlation measurement system for 3D displacement reconstruction ^[100]; (b) 3D diffraction-assisted fluorescent microscopy for 3D displacement reconstruction ^[101]

Fig. 17. 3D integral imaging using a diffraction grating^[106]. (a) Diffraction-grating-based image capture and computational reconstruction under multi-wavelength laser illumination; (b) 3D reconstructed images from parallax image arrays obtained with different wavelengths

Fig. 18. Calibration of 3D imaging system using planar mirrors^[109]. (a) Respective calibration for two virtual cameras; (b) direct calibration for system parameters.

Fig. 19. Calibration of 3D imaging system using a conic mirror^[111]. (a) Conic mirror structure with multiple control points on mirror base; (b) a calibration image with control points captured by system

Fig. 20. Model-free distortion correction method for biprism-based 3D imaging system^[112]

Fig. 21. Calibration method for rotational-prism-based variable-boresight 3D imaging system^[116]