A non-Lambertian photometric stereo under perspective projection (original) (raw)

References

• Agrawal A, Raskar R, Nayar SK, et al., 2005. Removing photography artifacts using gradient projection and flash-exposure sampling. ACM Trans Graph, 24(3):828–835. https://doi.org/10.1145/1073204.1073269
  Article Google Scholar
• Agrawal A, Raskar R, Chellappa R, 2006a. Edge suppression by gradient field transformation using cross-projection tensors. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.2301–2308. https://doi.org/10.1109/CVPR.2006.106
• Agrawal A, Raskar R, Chellappa R, 2006b. What is the range of surface reconstructions from a gradient field? 9th European Conf on Computer Vision, p.578–591. https://doi.org/10.1007/11744023_45
• Barsky S, Petrou M, 2003. The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Trans Patt Anal Mach Intell, 25(10):1239–1252. https://doi.org/10.1109/TPAMI.2003.1233898
  Article Google Scholar
• Chandraker M, Agarwal S, Kriegman D, 2007. Shadowcuts: photometric stereo with shadows. IEEE Conf on Computer Vision and Pattern Recognition, p.1–8. https://doi.org/10.1109/CVPR.2007.383288
• Chung HS, Jia JY, 2008. Efficient photometric stereo on glossy surfaces with wide specular lobes. IEEE Conf on Computer Vision and Pattern Recognition, p.1–8. https://doi.org/10.1109/CVPR.2008.4587771
• Coleman ENJr, Jain R, 1982. Obtaining 3-dimensional shape of textured and specular surfaces using four-source photometry. Comput Graph Image Process, 18(4):309–328. https://doi.org/10.1016/0146-664X(82)90001-6
  Article Google Scholar
• Donoho DL, 2006. Compressed sensing. IEEE Trans Inform Theory, 52(4):1289–1306. https://doi.org/10.1109/TIT.2006.871582
  Article MathSciNet MATH Google Scholar
• Esteban CH, Vogiatzis G, Cipolla R, 2008. Multiview photometric stereo. IEEE Trans Patt Anal Mach Intell, 30(3):548–554. https://doi.org/10.1109/TPAMI.2007.70820
  Article Google Scholar
• Finlayson GD, Schaefer G, 2001a. Convex and non-convex illuminant constraints for dichromatic colour constancy. Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.598–604 https://doi.org/10.1109/CVPR.2001.990528
• Finlayson GD, Schaefer G, 2001b. Solving for colour constancy using a constrained dichromatic reflection model. Int J Comput Vis, 42(3):127–144. https://doi.org/10.1023/A:1011120214885
  Article MATH Google Scholar
• Gabay D, Mercier B, 1976. A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput Math Appl, 2(1):17–40. https://doi.org/10.1016/0898-1221(76)90003-1
  Article MATH Google Scholar
• Galliani S, Lasinger K, Schindler K, 2015. Massively parallel multiview stereopsis by surface normal diffusion. Proc IEEE Int Conf on Computer Vision, p.873–881. https://doi.org/10.1109/ICCV.2015.106
• Georghiades AS, 2003. Incorporating the Torrance and Sparrow model of reflectance in uncalibrated photometric stereo. Proc 9th IEEE Int Conf on Computer Vision, Article 816. https://doi.org/10.1109/ICCV.2003.1238432
• Ghosh A, Chen TB, Peers P, et al., 2009. Estimating specular roughness and anisotropy from second order spherical gradient illumination. Comput Graph For, 28(4):1161–1170. https://doi.org/10.1111/j.1467-8659.2009.01493.x
  Google Scholar
• Goldman DB, Curless B, Hertzmann A, et al., 2010. Shape and spatially-varying BRDFs from photometric stereo. IEEE Trans Patt Anal Mach Intell, 32(6):1060–1071. https://doi.org/10.1109/TPAMI.2009.102
  Article Google Scholar
• Guo XJ, Cao XC, Ma Y, 2014. Robust separation of reflection from multiple images. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2187–2194. https://doi.org/10.1109/CVPR.2014.281
• Han BJ, Sim JY, 2018. Glass reflection removal using co-saliency-based image alignment and low-rank matrix completion in gradient domain. IEEE Trans Image Process, 27(10):4873–4888. https://doi.org/10.1109/TIP.2018.2849880
  Article MathSciNet MATH Google Scholar
• Han TQ, Shen HL, 2015. Photometric stereo for general BRDFs via reflection sparsity modeling. IEEE Trans Image Process, 24(12):4888–4903. https://doi.org/10.1109/TIP.2015.2471081
  Article MathSciNet MATH Google Scholar
• He XT, Peng YX, 2019. Fine-grained visual-textual representation learning. IEEE Trans Circ Syst Video Technol, online. https://doi.org/10.1109/TCSVT.2019.2892802
  Google Scholar
• He XT, Peng YX, Zhao JJ, 2019a. Fast fine-grained image classification via weakly supervised discriminative localization. IEEE Trans Circ Syst Video Technol, 29(5):1394–1407. https://doi.org/10.1109/TCSVT.2018.2834480
  Article Google Scholar
• He XT, Peng YX, Zhao JJ, 2019b. Which and how many regions to gaze: focus discriminative regions for finegrained visual categorization. Int J Comput Vis, 127(9):1235–1255. https://doi.org/10.1007/s11263-019-01176-2
  Article Google Scholar
• Huynh CP, Robles-Kelly A, 2010. A solution of the dichromatic model for multispectral photometric invariance. Int J Comput Vis, 90(1):1–27. https://doi.org/10.1007/s11263-010-0333-y
  Article Google Scholar
• Ikehata S, 2018. CNN-PS: CNN-based photometric stereo for general non-convex surfaces. European Conf on Computer Vision, p.3–19. https://doi.org/10.1007/978-3-030-01267-0_1
• Ikehata S, Aizawa K, 2014. Photometric stereo using constrained bivariate regression for general isotropic surfaces. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2179–2186. https://doi.org/10.1109/CVPR.2014.280
• Ikehata S, Wipf D, Matsushita Y, et al., 2012. Robust photometric stereo using sparse regression. IEEE Conf on Computer Vision and Pattern Recognition, p.318–325. https://doi.org/10.1109/CVPR.2012.6247691
• Kong N, Tai YW, Shin SY, 2012. A physically-based approach to reflection separation. IEEE Conf on Computer Vision and Pattern Recognition, p.9–16. https://doi.org/10.1109/CVPR.2012.6247652
• Levin A, Weiss Y, 2007. User assisted separation of reflections from a single image using a sparsity prior. IEEE Trans Patt Anal Mach Intell, 29(9):1647–1654. https://doi.org/10.1109/TPAMI.2007.1106
  Article Google Scholar
• Li Y, Brown MS, 2013. Exploiting reflection change for automatic reflection removal. Proc IEEE Int Conf on Computer Vision, p.2432–2439. https://doi.org/10.1109/ICCV.2013.302
• Mallick SP, Zickler TE, Kriegman DJ, et al., 2005. Beyond Lambert: reconstructing specular surfaces using color. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.619–626. https://doi.org/10.1109/CVPR.2005.88
• Nayar SK, Fang XS, Boult T, 1997. Separation of reflection components using color and polarization. Int J Comput Vis, 21(3):163–186. https://doi.org/10.1023/A:1007937815113
  Article Google Scholar
• Papadhimitri T, Favaro P, 2013. A new perspective on uncalibrated photometric stereo. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1474–1481. https://doi.org/10.1109/CVPR.2013.194
• Peng YX, He XT, Zhao JJ, 2018. Object-part attention model for fine-grained image classification. IEEE Trans Image Process, 27(3):1487–1500. https://doi.org/10.1109/TIP.2017.2774041
  Article MathSciNet MATH Google Scholar
• Quéau Y, Wu T, Lauze F, et al., 2017. A non-convex variational approach to photometric stereo under inaccurate lighting. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.350–359. https://doi.org/10.1109/CVPR.2017.45
• Quéau Y, Durix B, Wu T, et al., 2018. LED-based photometric stereo: modeling, calibration and numerical solution. J Math Imag Vis, 60(3):313–340. https://doi.org/10.1007/s10851-017-0761-1
  Article MathSciNet MATH Google Scholar
• Santo H, Samejima M, Sugano Y, et al., 2017. Deep photometric stereo network. Proc IEEE Int Conf on Computer Vision, p.501–509. https://doi.org/10.1109/ICCVW.2017.66
• Schechner YY, Kiryati N, Basri R, 2000. Separation of transparent layers using focus. Int J Comput Vis, 39(1):25–39. https://doi.org/10.1023/A:1008166017466
  Article MATH Google Scholar
• Shafer SA, 1985. Using color to separate reflection components. Color Res Appl, 10(4):210–218. https://doi.org/10.1002/col.5080100409
  Article Google Scholar
• Shen HL, Han TQ, Li CG, 2017. Efficient photometric stereo using kernel regression. IEEE Trans Image Process, 26(1):439–451. https://doi.org/10.1109/TIP.2016.2627805
  Article MathSciNet MATH Google Scholar
• Shi BX, Tan P, Matsushita Y, et al., 2014. Bi-polynomial modeling of low-frequency reflectances. IEEE Trans Patt Anal Mach Intell, 36(6):1078–1091. https://doi.org/10.1109/TPAMI.2013.196
  Article Google Scholar
• Shi BX, Mo ZP, Wu Z, et al., 2019. A benchmark dataset and evaluation for non-Lambertian and uncalibrated photometric stereo. IEEE Trans Patt Anal Mach Intell, 41(2):271–284. https://doi.org/10.1109/TPAMI.2018.2799222
  Article Google Scholar
• Shih Y, Krishnan D, Durand F, et al., 2015. Reflection removal using ghosting cues. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.3193–3201. https://doi.org/10.1109/CVPR.2015.7298939
• Solomon F, Ikeuchi K, 1996. Extracting the shape and roughness of specular lobe objects using four light photometric stereo. IEEE Trans Patt Anal Mach Intell, 18(4):449–454. https://doi.org/10.1109/34.491627
  Article Google Scholar
• Sun C, Liu SC, Yang TT, et al., 2016. Automatic reflection removal using gradient intensity and motion cues. Proc 24th ACM Int Conf on Multimedia, p.466–470. https://doi.org/10.1145/2964284.2967264
• Sunkavalli K, Zickler T, Pfister H, 2010. Visibility subspaces: uncalibrated photometric stereo with shadows. 11th European Conf on Computer Vision, p.251–264. https://doi.org/10.1007/978-3-642-15552-9_19
• Tan RT, Ikeuchi K, 2008. Separating reflection components of textured surfaces using a single image. IEEE Trans Patt Anal Mach Intell, 27(2):353–384. https://doi.org/10.1109/TPAMI.2005.36
  Google Scholar
• Tankus A, Kiryati N, 2005. Photometric stereo under perspective projection. 10th IEEE Int Conf on Computer Vision, p.611–616. https://doi.org/10.1109/ICCV.2005.190
• Wan RJ, Shi BX, Hwee TA, et al., 2016. Depth of field guided reflection removal. IEEE Int Conf on Image Processing, p.21–25. https://doi.org/10.1109/ICIP.2016.7532311
• Wan RJ, Shi BX, Duan LY, et al., 2018. CRRN: multi-scale guided concurrent reflection removal network. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.4777–4785.
• Wang F, Ainouz S, Petitjean C, et al., 2017. Specularity removal: a global energy minimization approach based on polarization imaging. Comput Vis Image Underst, 158:31–39. https://doi.org/10.1016/j.cviu.2017.03.003
  Article Google Scholar
• Woodham RJ, 1979. Photometric stereo: a reflectance map technique for determining surface orientation from image intensity. 22nd Annual Technical Symp on Image Understanding Systems and Industrial Applications I, p.136–143. https://doi.org/10.1117/12.956740
• Wu L, Ganesh A, Shi B, et al., 2010. Robust photometric stereo via low-rank matrix completion and recovery. 10th Asian Conf on Computer Vision, p.703–717. https://doi.org/10.1007/978-3-642-19318-7_55
• Yang J, Gong D, Liu LQ, et al., 2018. Seeing deeply and bidirectionally: a deep learning approach for single image reflection removal. Proc European Conf on Computer Vision, p.654–669. https://doi.org/10.1007/978-3-030-01219-9_40
• Yang QX, Tang JH, Ahuja N, 2015. Efficient and robust specular highlight removal. IEEE Trans Patt Anal Mach Intell, 37(6):1304–1311. https://doi.org/10.1109/TPAMI.2014.2360402
  Article Google Scholar
• Yeung SK, Wu TP, Tang CK, et al., 2015. Normal estimation of a transparent ob ject using a video. IEEE Trans Patt Anal Mach Intell, 37(4):890–897. https://doi.org/10.1109/TPAMI.2014.2346195
  Article Google Scholar
• Yu C, Seo Y, Lee SW, 2010. Photometric stereo from maximum feasible Lambertian reflections. 11th European Conf on Computer Vision, p.115–126. https://doi.org/10.1007/978-3-642-15561-1_9
• Zhang XE, Ng R, Chen QF, 2018. Single image reflection separation with perceptual losses. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.4786–4794. https://doi.org/10.1109/CVPR.2018.00503
• Zhou ZL, Wu Z, Tan P, 2013. Multi-view photometric stereo with spatially varying isotropic materials. IEEE Conf on Computer Vision and Pattern Recognition, p.1482–1489. https://doi.org/10.1109/CVPR.2013.195

Download references