Skip to main content

Decomposing the Tangent of Occluding Boundaries According to Curvatures and Torsions

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13692))

Included in the following conference series:

  • 2355 Accesses

Abstract

This paper develops new insight into the local structure of occluding boundaries on 3D surfaces. Prior literature has addressed the relationship between 3D occluding boundaries and their 2D image projections by radial curvature, planar curvature, and Gaussian curvature. Occluding boundaries have also been studied implicitly as intersections of level surfaces, avoiding their explicit description in terms of local surface geometry. In contrast, this work studies and characterizes the local structure of occluding curves explicitly in terms of the local geometry of the surface. We show how the first order structure of the occluding curve (its tangent) can be extracted from the second order structure of the surface purely along the viewing direction, without the need to consider curvatures or torsions in other directions. We derive a theorem to show that the tangent vector of the occluding boundary exhibits a strikingly elegant decomposition along the viewing direction and its orthogonal tangent, where the decomposition weights precisely match the geodesic torsion and the normal curvature of the surface respectively only along the line-of-sight! Though the focus of this paper is an enhanced theoretical understanding of the occluding curve in the continuum, we nevertheless demonstrate its potential numerical utility in a straight-forward marching method to explicitly trace out the occluding curve. We also present mathematical analysis to show the relevance of this theory to computer vision and how it might be leveraged in more accurate future algorithms for 2D/3D registration and/or multiview stereo reconstruction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This assumes no radial/tangential lens distortion, otherwise we must also multiply the right hand side of (6) by the determinant of the distortion model’s Jacobian.

References

  1. Abdel-All, N.H., Badr, S.A.N., Soliman, M., Hassan, S.A.: Intersection curves of two implicit surfaces in r3. J. Math. Comput. Sci. 2(2), 152–171 (2012)

    MathSciNet  Google Scholar 

  2. Abdel-Malek, K., Yeh, H.J.: On the determination of starting points for parametric surface intersections. Comput. Aided Des. 29(1), 21–35 (1997)

    Article  Google Scholar 

  3. Bajaj, C.L., Hoffmann, C.M., Lynch, R.E., Hopcroft, J.: Tracing surface intersections. Comput. Aided Geomet. Des. 5(4), 285–307 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chen, H.C., et al.: Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration. Meas. Sci. Technol. 24(10), 105701 (2013)

    Article  Google Scholar 

  5. Dambreville, S., Sandhu, R., Yezzi, A., Tannenbaum, A.: Robust 3D Pose estimation and efficient 2D region-based segmentation from a 3D shape prior. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 169–182. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88688-4_13

    Chapter  Google Scholar 

  6. De Vivo, F., Battipede, M., Gili, P.: Occlusion points identification algorithm. Comput. Aided Des. 91, 75–83 (2017)

    Article  Google Scholar 

  7. DeCarlo, D., Finkelstein, A., Rusinkiewicz, S., Santella, A.: Suggestive contours for conveying shape. In: ACM SIGGRAPH 2003 Papers. pp. 848–855 (2003)

    Google Scholar 

  8. Düldül, B.U., Çalişkan, M.: The geodesic curvature and geodesic torsion of the intersection curve of two surfaces. Acta Universitatis Apulensis. Math. Informat 24, 161–172 (2010)

    MathSciNet  MATH  Google Scholar 

  9. Fabbri, R., Kimia, B.B.: Multiview differential geometry of curves. Int. J. Comput. Vision 120(3), 324–346 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  10. Faugeras, O., Keriven, R.: Variational principles, surface evolution, PDE’s, level set methods and the stereo problem. IEEE Trans. Image Process. 7, 336–344 (2002)

    Google Scholar 

  11. Giblin, P.: Reconstruction of surfaces from profiles. In: Proceedings of 1st International Conference on Computer Vision, London, 1987 (1987)

    Google Scholar 

  12. Ikeuchi, K., Horn, B.K.: Numerical shape from shading and occluding boundaries. Artif. Intell. 17(1–3), 141–184 (1981)

    Article  MATH  Google Scholar 

  13. Kaptein, B., Valstar, E., Stoel, B., Rozing, P., Reiber, J.: A new model-based RSA method validated using cad models and models from reversed engineering. J. Biomech. 36(6), 873–882 (2003)

    Article  Google Scholar 

  14. Kehtarnavaz, N., Defigueiredo, R.: Recognition of 3D curves based on curvature and torsion. In: Digital and Optical Shape Representation and Pattern Recognition. vol. 938, pp. 357–364. International Society for Optics and Photonics (1988)

    Google Scholar 

  15. Koenderink, J.J.: What does the occluding contour tell us about solid shape? Perception 13(3), 321–330 (1984)

    Article  Google Scholar 

  16. Kolev, K., Klodt, M., Brox, T., Cremers, D.: Continuous global optimization in multiview 3D reconstruction. Int. J. Comput. Vision 84(1), 80–96 (2009)

    Article  Google Scholar 

  17. Kriegman, D.J., Ponce, J.: A new curve tracing algorithm and some applications. In: Curves and Surfaces, pp. 267–270. Elsevier, Philadelphia (1991)

    Google Scholar 

  18. Kriegman, D.J., Ponce, J.: Geometric modeling for computer vision. In: Curves and Surfaces in Computer Vision and Graphics II, vol. 1610, pp. 250–260. International Society for Optics and Photonics (1992)

    Google Scholar 

  19. Lamecker, H., Wenckebach, T.H., Hege, H.C.: Atlas-based 3D-shape reconstruction from X-ray images. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 1, pp. 371–374. IEEE (2006)

    Google Scholar 

  20. Lazebnik, S., Ponce, J.: The local projective shape of smooth surfaces and their outlines. Int. J. Comput. Vision 63(1), 65–83 (2005)

    Article  MATH  Google Scholar 

  21. Li, T.M., Aittala, M., Durand, F., Lehtinen, J.: Differentiable monte CARLO ray tracing through edge sampling. ACM Trans. Graph. 37(6), 1–11 (2018)

    Article  Google Scholar 

  22. Lindstrom, P., Turk, G.: Fast and memory efficient polygonal simplification. In: Proceedings Visualization’98 (Cat. No. 98CB36276,. pp. 279–286. IEEE (1998)

    Google Scholar 

  23. Lone, M.S., Shahid, M.H., Sharma, S.: A new approach towards transversal intersection curves of two surfaces in \(\mathbb{R} ^3\). Geom. Imag. Comput. 3(3), 81–99 (2016)

    Article  MATH  Google Scholar 

  24. Marr, D.: Analysis of occluding contour. Proc. R. Soc. Lond. Ser. B. Biol. Sci. 197(1129), 441–475 (1977)

    Google Scholar 

  25. Ponce, J., Hebert, M.: On image contours of projective shapes. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 736–749. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_48

    Chapter  Google Scholar 

  26. Prisacariu, V.A., Reid, I.D.: PWP3D: real-time segmentation and tracking of 3D objects. Int. J. Comput. Vis. 98(3), 335–354 (2012)

    Article  MathSciNet  Google Scholar 

  27. Runz, M., et al.: Frodo: From detections to 3D objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14720–14729 (2020)

    Google Scholar 

  28. Sandhu, R., Dambreville, S., Yezzi, A., Tannenbaum, A.: A nonrigid kernel-based framework for 2D–3D pose estimation and 2D image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1098–1115 (2010)

    Article  MATH  Google Scholar 

  29. Soliman, M.A.L., Abdel-All, N.H., Hassan, S.A., Badr, S.A.N., et al.: Intersection curves of implicit and parametric surfaces in r 3. Appl. Math. 2(08), 1019 (2011)

    Article  MathSciNet  Google Scholar 

  30. Thirion, J.P., Gourdon, A.: The 3D marching lines algorithm. Graph. Models Image Process. 58(6), 503–509 (1996)

    Article  Google Scholar 

  31. Vaillant, R.: Using occluding contours for 3D object modeling. In: Faugeras, O. (ed.) ECCV 1990. LNCS, vol. 427, pp. 454–464. Springer, Heidelberg (1990). https://doi.org/10.1007/BFb0014895

    Chapter  Google Scholar 

  32. Vaillant, R., Faugeras, O.D.: Using extremal boundaries for 3-d object modeling. IEEE Trans. Pattern Anal. Mach. Intell. 14(02), 157–173 (1992)

    Article  Google Scholar 

  33. Wang, C., Fu, H., Tao, D., Black, M.: Occlusion boundary: A formal definition & its detection via deep exploration of context. IEEE Trans. Pattern Anal. Mach. Intell. 44, 2641–2656 (2020)

    Google Scholar 

  34. Ye, X., Maekawa, T.: Differential geometry of intersection curves of two surfaces. Comput. Aided Geomet. Des. 16(8), 767–788 (1999)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huizong Yang .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 198 KB)

A Appendices

A Appendices

1.1 A.1 Intrinsic Gradient

If \(f:\mathcal {S}\rightarrow \mathbb {R}\) is a differentiable function defined on \(\mathcal {S}\), the “intrinsic gradient” would naturally correspond to the projection onto the tangent plane of the standard \(\mathbb {R}^{3}\) gradient of any local differentiable extension \(\hat{f}:\mathbb {R}^{3}\rightarrow \mathbb {R}\), where \(\hat{f}(S)=f(S)\).

$$\nabla _{S}f=\nabla \hat{f}-\left( \nabla \hat{f}\cdot \textbf{n}\right) \textbf{n}$$

However, we can intrinsically define the gradient of f, without reference to any extension function \(\hat{f}\), as the unique tangent vector \(\nabla _{S}f\) which satisfies the equality \(\partial _\textbf{t}f=\nabla _{S}f\cdot \textbf{t}\) for any tangent vector \(\textbf{t}\) (where \(\partial _\textbf{t}f\) denotes the directional derivative of f along the vector \(\textbf{t}\)). We can solve for this vector using the first fundamental form coefficients E, F, G together with the partial derivatives of f with respect to the surface parameters uv as follows.

$$\begin{aligned} \nabla _{S}f = \begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix} \begin{bmatrix}E &{} F\\ F &{} G \end{bmatrix}^{-1} \begin{bmatrix} \frac{\partial f}{\partial u}\\ \\ \frac{\partial f}{\partial v} \end{bmatrix} \end{aligned}$$

1.2 A.2 Orthogonal Decomposition of the Shape Operator

We first orthogonally decompose the shape operator (which actually generalizes Theorem 1) to see both its covariant action and its contravariant action which is ignored in classical differential geometry but highly relevant to our exploration.

Lemma 1

The action of the shape operator \(\mathbb {S}\) on any tangent vector \(\textbf{t} \in \mathbb {R}^3\) can be decomposed into a covariant component parallel to its argument \(\textbf{t}\) and a contravariant component along the perpendicular tangent direction \(\textbf{t}_{\perp }=\textbf{n}\times \textbf{t}\)

$$\begin{aligned} \mathbb {S}(\textbf{t}) = \kappa (\textbf{t})\;\textbf{t}+{\tau }(\textbf{t})\;\textbf{t}_{\perp } \end{aligned}$$

where \(\kappa (\textbf{t})\) is the normal curvature (by definition) in the direction of \(\textbf{t}\) and where \(\tau (\textbf{t})\) matches the geodesic torsion in the direction of \(\textbf{t}\).

Proof

As an operator from the tangent space back to the tangent space, we may decompose the action of the shape operator into two orthogonal directions within the tangent plane, one parallel to its argument \(\textbf{t}\) and the other along the perpendicular tangent direction \(\textbf{t}_{\perp }\) as follows,

$$\begin{aligned} \mathbb {S}(\textbf{t})&=\left( \mathbb {S}(\textbf{t})\cdot \frac{\textbf{t}}{\Vert \textbf{t}\Vert }\right) \frac{\textbf{t}}{\Vert \textbf{t}\Vert }+\left( \mathbb {S}(\textbf{t})\cdot \frac{\textbf{t}_{\perp }}{\Vert \textbf{t}\Vert }\right) \frac{\textbf{t}_{\perp }}{\Vert \textbf{t}\Vert }\nonumber \\&=\underbrace{\left( \mathbb {S}\left( \frac{\textbf{t}}{\Vert \textbf{t}\Vert }\right) \cdot \frac{\textbf{t}}{\Vert \textbf{t}\Vert }\right) }_{\kappa (\textbf{t})}\textbf{t}+\underbrace{\left( \mathbb {S}\left( \frac{\textbf{t}}{\Vert \textbf{t}\Vert }\right) \cdot \frac{\textbf{t}_{\perp }}{\Vert \textbf{t}\Vert }\right) }_{\tau (\textbf{t})}\textbf{t}_{\perp } \nonumber \\&=\kappa (\textbf{t})\textbf{t} + \tau (\textbf{t})\textbf{t}_{\perp } \end{aligned}$$
(8)

1.3 A.3 Proof for Theorem 1

Proof

We first compute the intrinsic gradient of the local occlusion function g.

$$\begin{aligned} \nabla _{\!{}_{S}}g&=\begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix}\begin{bmatrix}E &{} F\\ F &{} G \end{bmatrix}^{-1}\nabla _{u,v}\left( (S-P)\cdot \textbf{n}\right) \\&=\begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix}\begin{bmatrix}E &{} F\\ F &{} G \end{bmatrix}^{-1}\begin{bmatrix}\underbrace{\frac{\partial S}{\partial u}\cdot \textbf{n}}_{0}+\underbrace{(S-P)}_{r\textbf{e}_{r}}\cdot \frac{\partial N}{\partial u}\\ \underbrace{\frac{\partial S}{\partial v}\cdot \textbf{n}}_{0}+\underbrace{(S-P)}_{r\textbf{e}_{r}}\cdot \frac{\partial N}{\partial v} \end{bmatrix}\\&=r\,\begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix}\begin{bmatrix}E &{} F\\ F &{} G \end{bmatrix}^{-1}\begin{bmatrix}\frac{\partial N}{\partial u}\cdot \textbf{e}_{r}\\ {\mathop {\frac{\partial N}{\partial v}\cdot \textbf{e}_{r}}\limits ^{}} \end{bmatrix} \end{aligned}$$

Notice that

$$\begin{aligned} \frac{\partial N}{\partial u}\cdot \textbf{t}_{r}&=\frac{\partial N}{\partial u}\cdot \left( \textbf{e}_{r}-(\textbf{e}_{r}\cdot \textbf{n})\textbf{n}\right) =\frac{\partial N}{\partial u}\cdot \textbf{e}_{r}-(\textbf{e}_{r}\cdot \textbf{n})\underbrace{\frac{\partial N}{\partial u}\cdot \textbf{n}}_{0}\\ \frac{\partial N}{\partial v}\cdot \textbf{t}_{r}&=\frac{\partial N}{\partial v}\cdot \left( \textbf{e}_{r}-(\textbf{e}_{r}\cdot \textbf{n})\textbf{n}\right) =\frac{\partial N}{\partial v}\cdot \textbf{e}_{r}-(\textbf{e}_{r}\cdot \textbf{n})\underbrace{\frac{\partial N}{\partial u}\cdot \textbf{n}}_{0} \end{aligned}$$

which allows us to substitute \(\textbf{e}_{r}\) with \(\textbf{t}_{r}=\begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix}t_{r}\), where \(t_{r}\) denotes the 2D intrinsic representation of \(\textbf{t}_{r}\) in the basis \(\frac{\partial S}{\partial u}\) and \(\frac{\partial S}{\partial v}\)

$$\begin{aligned} \nabla _{_{S}}g&=r\,\begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix}\begin{bmatrix}E &{} F\\ F &{} G \end{bmatrix}^{-1}\begin{bmatrix}\frac{\partial N}{\partial u}&\frac{\partial N}{\partial v}\end{bmatrix}^T\begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix}t_{r} \\ {}&= -r\,\underbrace{\begin{bmatrix}\frac{\partial S}{\partial u}&\frac{\partial S}{\partial v}\end{bmatrix}\begin{bmatrix}E &{} F\\ F &{} G \end{bmatrix}^{-1}\begin{bmatrix}e &{} f\\ f &{} g \end{bmatrix}t_{r}}_{\text{ Weingarten }} \end{aligned}$$

where efg are the coefficients of second fundamental form \(\textbf{II}\). The appearance of the Weingarten formulas allows us to recognize this part of the expression as the shape operator \(\mathbb {S}\) applied to the radial tangent vector \(\textbf{t}_{r}\) and therefore write

$$\begin{aligned} \frac{\nabla _{_{S}}g}{r} =-\mathbb {S}(\textbf{t}_{r}) \end{aligned}$$
(9)

Since we define in Lemma 1 the direction of \(\textbf{t}_\perp ^*\) opposite to direction of \(\nabla _{_S}g\), we may then write

$$\begin{aligned} \textbf{t}_{\perp }^{*}=-\frac{\nabla _{_{S}}g}{r}=\mathbb {S}(\textbf{t}_r)=\kappa _{r}\,\textbf{t}_{r}+\tau _{r}\,\textbf{t}_{r\perp } \end{aligned}$$

Subsequently, the occluding tangent is computed as follows,

$$\begin{aligned} \textbf{t}^{*}&=-\textbf{n}\times \textbf{t}_{\perp }^{*} =-\kappa _{r}\,\textbf{n}\times \textbf{e}_{r}-\tau _{r}\,\textbf{n}\times \left( \textbf{n}\times \textbf{e}_{r}\right) \\&=-\kappa _{r}\,\textbf{n}\times \textbf{e}_{r}-\tau _{r}\,\left( \textbf{n}\underbrace{(\textbf{n}\cdot \textbf{e}_{r})}_{\frac{g(S)}{r}=0}-\textbf{e}_{r}\underbrace{(\textbf{n}\cdot \textbf{n})}_{1}\right) =-\kappa _{r}\,\textbf{n}\times \textbf{e}_{r}+\tau _{r}\,\textbf{e}_{r} \end{aligned}$$

For the outward unit normal \(\textbf{n}\), we negate \(\textbf{n}\) in the equations above. Therefore, we have

$$\begin{aligned} \textbf{t}^{*}={\left\{ \begin{array}{ll} \kappa _{r}\,(\textbf{e}_{r}\times \textbf{n})+\tau _{r}\,\textbf{e}_{r}, &{} \text{ for } \text{ inward } \textbf{n}\\ \kappa _{r}\,(\textbf{n}\times \textbf{e}_{r})+\tau _{r}\,\textbf{e}_{r} &{} \text{ for } \text{ outward } \textbf{n} \end{array}\right. } \end{aligned}$$

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, H., Yezzi, A. (2022). Decomposing the Tangent of Occluding Boundaries According to Curvatures and Torsions. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13692. Springer, Cham. https://doi.org/10.1007/978-3-031-19824-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19824-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19823-6

  • Online ISBN: 978-3-031-19824-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics