Depth and Surface Normal Estimation from a Single Image Mian Wei - - PowerPoint PPT Presentation

depth and surface normal estimation from a single image
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Depth and Surface Normal Estimation from a Single Image Mian Wei - - PowerPoint PPT Presentation

1 Depth and Surface Normal Estimation from a Single Image Mian Wei University of Toronto 2 Indirect-Invariant What is the problem? 3 Indirect-Invariant Given one image 4 N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, Indoor


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Mian Wei

University of Toronto

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Depth and Surface Normal Estimation from a Single Image

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Indirect-Invariant What is the problem?

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Indirect-Invariant Given one image

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  • N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support

inference from RGBD images,” in Proc. Eur. Conf. Comput. Vision, 2012, pp. 746–760.

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Indirect-Invariant Estimate the following:

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Eigen, D. and Fergus, R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. ICCV 2015

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Eigen, D. and Fergus, R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. ICCV 2015

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Indirect-Invariant Why is this hard?

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Indirect-Invariant Multiple ambiguities

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Indirect-Invariant Scale ambiguity

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Indirect-Invariant Bas-relief ambiguity

  • P. Belhumeur, D. Kriegman, and A. Yuille, “The Bas-Relief Ambiguity,” Proc. IEEE Conf.

Computer Vision and Pattern Recognition, pp. 1040-1046, 1997.

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Indirect-Invariant Let’s play a game

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Indirect-Invariant Spot the Difference

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  • P. Belhumeur, D. Kriegman, and A. Yuille, “The Bas-Relief Ambiguity,” Proc. IEEE Conf.

Computer Vision and Pattern Recognition, pp. 1040-1046, 1997.

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  • P. Belhumeur, D. Kriegman, and A. Yuille, “The Bas-Relief Ambiguity,” Proc. IEEE Conf.

Computer Vision and Pattern Recognition, pp. 1040-1046, 1997.

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Indirect-Invariant All the same

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  • P. Belhumeur, D. Kriegman, and A. Yuille, “The Bas-Relief Ambiguity,” Proc. IEEE Conf.

Computer Vision and Pattern Recognition, pp. 1040-1046, 1997.

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  • P. Belhumeur, D. Kriegman, and A. Yuille, “The Bas-Relief Ambiguity,” Proc. IEEE Conf.

Computer Vision and Pattern Recognition, pp. 1040-1046, 1997.

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Indirect-Invariant Family of transformation

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Indirect-Invariant Generalized Bas-Relief

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Indirect-Invariant Change shape and illumination

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Indirect-Invariant Yield same image

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Indirect-Invariant Existing works

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Indirect-Invariant Multi-view Stereo

Hartley,R. and Zisserman, A. 2000. Multiple view geometry in computer vision, Cambridge University Press: Cambridge, UK.

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Indirect-Invariant Photometric Stereo

Woodham, R.J. (1980), Photometric method for determining surface orientation from multiple images, Optical Engineering 19 (1) 139-144.

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Indirect-Invariant Collimated Light Sources

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Indirect-Invariant Light rays parallel

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Indirect-Invariant Shape from Focus

  • S. Nayar and N. Yasuo, “Shape From Focus,” IEEE Trans. Pattern Analysis and Machine

Intelligence, vol. 16, no. 8, pp. 824-831, 1994.

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Indirect-Invariant Light Fall-off Stereo

  • M. Liao, L. Wang, R. Yang, and M. Gong. Light fall-off stereo. In Proceedings of CVPR,

pages 1–8, 2007.

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Indirect-Invariant Specialized Hardware

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Indirect-Invariant Laser Scanner

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Indirect-Invariant Active Illumination

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Indirect-Invariant Time of Flight

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Indirect-Invariant Estimating Depth

  • D. Eigen, C. Puhrsch, and R. Fergus. Depth map prediction from a single image using a

multi-scale deep network. NIPS 2014

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Indirect-Invariant Train 2 networks

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Indirect-Invariant Global coarse-scale network

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Indirect-Invariant Local fine-scale network

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Indirect-Invariant Global coarse-scale network

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Indirect-Invariant Learns a coarse depth map

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Indirect-Invariant Used as input to local network

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Indirect-Invariant Intuition:

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Indirect-Invariant Coarse info learnt already

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Indirect-Invariant Focus on learning finer info

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Indirect-Invariant Scale ambiguity

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Indirect-Invariant Scale invariant error function

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D(y, y*) = 1 2n (log yi − log y*

i i=1 n

+α(yi, y*

i))2

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α(yi, y*

i) = 1

n (log y*

i − log yi) i=1 n

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D(ay,ay*) = 1 2n (logayi − logay*

i i=1 n

+α(ayi,ay*

i))2

D(ay,ay*) = 1 2n (loga − loga + log yi − log y*

i i=1 n

+α(ayi,ay*

i))2

D(ay,ay*) = 1 2n (log yi − log y*

i i=1 n

+ loga − loga +α(yi, y*

i))2

D(ay,ay*) = D(y, y*)

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Indirect-Invariant Loss Function

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Indirect-Invariant Scale invariant

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L(y, y*) = 1 n d 2

i i=1 n

− λ n2 ( di

i=1 n

)2 di = log yi − log y*

i

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Indirect-Invariant 2 Datasets

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Indirect-Invariant NYUDepthV2

  • N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support

inference from RGBD images,” in Proc. Eur. Conf. Comput. Vision, 2012, pp. 746–760.

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Indirect-Invariant Indoor Rooms

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Indirect-Invariant KITTI

  • A. Greiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The kitti dataset.

International Journal of Robotics Research (IJRR). 2013.

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Indirect-Invariant Outdoor images taken on a car

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Indirect-Invariant How do you get ground truth?

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Indirect-Invariant NYUDepthV2

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Indirect-Invariant Kinect

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Indirect-Invariant KITTI

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Indirect-Invariant Time of Flight

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Indirect-Invariant Times how long light travels

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Indirect-Invariant From light source to camera

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Indirect-Invariant Results

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Indirect-Invariant Estimating Surface Normals

  • X. Wang, D. F. Fouhey, and A. Gupta. Designing deep networks for surface normal
  • estimation. CVPR 2015
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Indirect-Invariant Similar to Eigen

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Indirect-Invariant Trains 3 networks

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Indirect-Invariant Global coarse-scale network

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Indirect-Invariant Trains for room layout as well

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Indirect-Invariant Local fine-scale network

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Indirect-Invariant Trains for edge labels as well

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Indirect-Invariant Convex, concave, occlusion, N/A

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Indirect-Invariant Difference: Global and Local trained separately

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Indirect-Invariant Fusion Network

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Indirect-Invariant Combines both networks

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Indirect-Invariant How to represent normals

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Indirect-Invariant Normals lie in continuous space

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Indirect-Invariant Regression as Classification

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Indirect-Invariant Surface normal triangular coding

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Indirect-Invariant Codebook with k-means

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Indirect-Invariant Delaunay Triangulation cover

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Indirect-Invariant Triangles as classes

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Indirect-Invariant Represent Surface Normals

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Indirect-Invariant Weighted sum of triangle corners

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Indirect-Invariant Loss Function

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L(I,Y) = − (

k=1 K

i=1 M×M

1(yi = k)logF

i,k(I))

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Indirect-Invariant Thoughts

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Indirect-Invariant Do not address bas-relief

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Indirect-Invariant Incorporate Computer Graphics

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Indirect-Invariant Inverse problem

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Indirect-Invariant Given surface normals

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Indirect-Invariant How should the scene look?

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Indirect-Invariant What is the correct image?

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Indirect-Invariant Incorporate image formation model

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Indirect-Invariant Why depth from single image