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A Fast Local Descriptor for Dense Matching

Engin Tola, Vincent Lepetit, Pascal FuaComputer Vision Laboratory, EPFL

Reporter： Jheng-You Lin

• Introduction• DAISY Computation• Results• Conclusion

Outline

• Wide-base line matching propose : SIFT、 GLOH、 SURF… (histogram based descriptor)– Good performance and robustness to image

transformations.– High computational cost and sensitivity to occlusions.

• Purpose– Design a descriptor that is as robust as SIFT or GLOH but

can be computed much more effectively and handle occlusions.

Introduction

• Novelty– introduces DAISY local image descriptor

Introduction (cont.)

• Novelty– introduces DAISY local image descriptor

Introduction (cont.)

* S. Winder and M. Brown. Learning Local Image Descriptors in CVPR’07

Improved performance : + Precise localization+ Rotational Robustness

• Novelty– introduces DAISY local image descriptor

Introduction (cont.)

Replacing weighted sums by convolutions

DAISY Computation

DAISY Computation

First compute gradient magnitude layers in different orientations

DAISY ComputationThen, apply convolution with a Gaussian kernel to pre-compute the histograms for every point

DAISY Computation

DAISY Computation

DAISY Computation

DAISY Computation

The computation mostly involves 1D convolutions, which is fast.

DAISY Computation

Rotating the descriptor only involves reordering the histograms.

…

DAISY Computation

Rotating the descriptor only involves reordering the histograms.

…

DAISY Computation

Computation Time Comparison(in seconds)

DAISY Computation

The full DAISY descriptor D(u, v) :

The descriptor of the same point that is close to an occlusion would be very different.

Normalize to unit norm

Results

Laser scan DAISY SIFT

SURF Pixel DifferenceNCC

Results

baseline increaseblock

Error threshold :

Top : 10%Middle : 5%Bottom : 1%

DAISY SIFT SURF

NCC

SURF Pixel Difference

Results

Using low-resolution of the Brussels images[24]

768x510 (2048x1360 origin)

[24] Combined Depth and Outlier Estimation in Multi-View Stereo, CVPR’06

Results

Using low-resolution of the Rathaus images[25]

768x512 (3072x2048 origin)

The holes are caused by the fact that a lot of the texture is not visible.

[25] Dense Matching of Multiple Wide-Baseline Views, ICCV’03

ResultsInput images Virtual view Synthesized

ResultsVirtual view Synthesized DAISY NCC

• Efficient descriptor and produces good reconstructions.

• Can handle low quality imagery

Conclusion