3D Model Matching with Viewpoint-Invariant

Patches(VIP)

Reporter：鄒嘉恆Date： 10/06/2009

Introduction

This paper introduces Viewpoint-invariant patch(VIP) for robust registration and large scale scene reconstruction.

Outline

Viewpoint-Invariant Patch(VIP)Hierarchical estimation of 3D similarity

transformationExperimental results and evaluationConclusion

VIP-Viewpoint normalizationWarp the image textureProject the textureExtract the VIP descriptor

VIP-VIP generationVIP is defined as (x, σ, n, d, s)

x : 3D positionσ： patch sizen： surface normald： dominant orientations： SIFT descriptor

Hierarchical estimation of 3D similarity transformation3D similarity transformation from a single

VIP correspondence(x1, σ1, n1, d1, s1), (x2, σ2, n2, d2, s2)

scaling：

rotation：

translation：

Hierarchical estimation of 3D similarity transformationHierarchical Efficient Hypothesis-

Test(HEHT) method3 stages：

ScalingRotationTranslation

Using RANSAC with VIP.

Experimental result and evaluationThe number of inlier correspondences.

The re-detection rate.

Experimental result and evaluationUse Structure from Motion(SfM) to

compute its depths map and camera positions for each sequence.

Camera positions were defined relative to the pose of the first camera in each sequence.

Experimental result and evaluation

Number of inliers

Re-detection rate

Experimental result and evaluationScene 1：

Experimental result and evaluationScene 2：

Experimental result and evaluationScene 3：

Experimental result and evaluation

Experimental result and evaluation

Conclusion

Their evaluation demonstrates that VIP features are an improvement on current methods for robust and accurate 3D model alighment.