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用于城市场景重建的分段平面和非平面立体

Piecewise Planar and Non-Planar Stereo for Urban Scene Reconstruction
课程网址: http://videolectures.net/cvpr2010_gallup_ppns/  
主讲教师: David Gallup
开课单位: 北卡罗来纳大学
开课时间: 2010-07-19
课程语种: 英语
中文简介:
用于立体声的分段平面模型最近已成为用于建模室内和城市户外景观的流行模型。强平面性假设克服了纹理不良表面所带来的挑战,并且导致用于渲染,存储和传输的低复杂度3D模型。然而,这样的模型在非平面物体的存在下表现不佳,例如,在许多场景中存在的灌木,树木和其他杂乱。我们提出了一种能够处理包含平面和非平面区域的更一般场景的立体方法。我们提出的技术将图像分割成分段平面区域以及标记为非平面的区域。非平面区域由标准多视图立体算法的结果建模。分割由多视图光致一致性以及基于colorandtexture的分类器的结果驱动,该分类器从手标记的平面和非平面图像区域学习。此外,我们的方法在多个重叠视图中链接和融合平面假设,确保在任意数量的图像上进行一致的3D重建。使用我们的系统,我们构建了数千帧街道视频。结果表明,我们的方法成功地恢复了分段平面表面以及包含大型建筑物和住宅的挑战性的一般3D表面。
课程简介: Piecewise planar models for stereo have recently become popular for modeling indoor and urban outdoor scenes. The strong planarity assumption overcomes the challenges presented by poorly textured surfaces, and results in low complexity 3D models for rendering, storage, and transmission. However, such a model performs poorly in the presence of non-planar objects, for example, bushes, trees, and other clutter present in many scenes. We present a stereo method capable of handling more general scenes containing both planar and non-planar regions. Our proposed technique segments an image into piecewise planar regions as well as regions labeled as non-planar. The nonplanar regions are modeled by the results of a standard multi-view stereo algorithm. The segmentation is driven by multi-view photoconsistency as well as the result of a colorand texture-based classifier, learned from hand-labeled planar and non-planar image regions. Additionally our method links and fuses plane hypotheses across multiple overlapping views, ensuring a consistent 3D reconstruction over an arbitrary number of images. Using our system, we have reconstructed thousands of frames of street-level video. Results show our method successfully recovers piecewise planar surfaces alongside general 3D surfaces in challenging scenes containing large buildings as well as residential houses.
关 键 词: 分段平面模型; 图像分割; 强平面性假设
课程来源: 视频讲座网
最后编审: 2020-07-16:yumf
阅读次数: 109