Constructing a Multivalued Representation for View Synthesis
International Journal of Computer Vision
MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
International Journal of Computer Vision
LAMP: 3D layered, adaptive-resolution, and multi-perspective panorama—a new scene representation
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Virtual object placement in video for augmented reality
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
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This paper focuses on the representation and view generation of three-dimensional (3-D) scenes. In contrast to existing methods that construct a full 3-D model or those that exploit geometric invariants, our representation consists of dense depth maps at several preselected viewpoints from an image sequence. Furthermore, instead of using multiple calibrated stationary cameras or range scanners, we derive our depth maps from image sequences captured by an uncalibrated camera with only approximately known motion. We propose an adaptive matching algorithm that assigns various confidence levels to different regions in the depth maps. Nonuniform bicubic spline interpolation is then used to fill in low confidence regions in the depth maps. Once the depth maps are computed at preselected viewpoints, the intensity and depth at these locations are used to reconstruct arbitrary views of the 3-D scene. Specifically, the depth maps are regarded as vertices of a deformable 2-D mesh, which are transformed in 3-D, projected to 2-D, and rendered to generate the desired view. Experimental results are presented to verify our approach