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3D visual ppt.ppt

发布时间：2022-06-13 发布人：admin 分类：说明书 资料大小：0.32M 资料格式：ppt 举报 版权申诉
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    Visual 3D Modeling from Images
    Introduction u Goal camera aroud n Automatically extract a realistic 3D model by freely moving a u Method n Using a sequence of images u Result n 3D model: a scaled version of original object
    Overall Flow Chart
    Feature Points u Principle of feature points extraction feature point should be sufficiently different from neighborhoods after a small dispacement: corner(角点) n n u Feature points matching 7x7 pixel widow n matching methods: a. sum-of-square differences(SSD:最小方差和) b. normalized cross-correlation(NCC:归一化互相关) Figure 1:corresponding corners
    Feature Points u Problems n scaling, rotation , lighting conditions... ->Matching using affinely in variant regions n outliers ->Robust algorithm:RANSAC (RANdom SAmpling Consensus:随机抽样一致性算 法) u Other approaches n n lines or curves SIFT（Scale-invariant feature transform）
    Projective Geometry:Pinhole camera model fX fY Z            f f      PXx  P diag  1           1 1 1 0 0 0      X Y Z 1             ( f , f 0|I)1,  P is the cmera projection matrix
    Projective Geometry:two-view geometry The foundametal matrix: x’T Fx = 0 Foundamental matrix F can be computed with 7 or 8 points
    Structure and motion u Select two views n n sufficient features are matched the views should not be too close to each other so that the initial structure is well-conditioned u Initial frame P = [I|0] and P’ = [R|t]
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