Scene reconstruction from high spatio-angular resolution light fields

    公开(公告)号:US09786062B2

    公开(公告)日:2017-10-10

    申请号:US13944337

    申请日:2013-07-17

    Abstract: The disclosure provides an approach for estimating depth in a scene. According to one aspect, regions where the depth estimation is expected to perform well may first be identified in full-resolution epipolar-plane images (EPIs) generated from a plurality of images of the scene. Depth estimates for EPI-pixels with high edge confidence are determined by testing a number of discrete depth hypotheses and picking depths that lead to highest color density of sampled EPI-pixels. The depth estimate may also be propagated throughout the EPIs. This process of depth estimation and propagation may be iterated until all EPI-pixels with high edge confidence have been processed, and all EPIs may also be processed in this manner. The EPIs are then iteratively downsampled to coarser resolutions, at which edge confidence for EPI-pixels not yet processed are determined, depth estimates of EPI-pixels with high edge confidence made, and depth estimates propagated throughout the EPIs.

    Sparse light field representation
    9.
    发明授权
    Sparse light field representation 有权
    稀疏光场表示

    公开(公告)号:US09412172B2

    公开(公告)日:2016-08-09

    申请号:US13944437

    申请日:2013-07-17

    Abstract: The disclosure provides an approach for generating a sparse representation of a light field. In one configuration, a sparse representation application receives a light field constructed from multiple images, and samples and stores a set of line segments originating at various locations in epipolar-plane images (EPI), until the EPIs are entirely represented and redundancy is eliminated to the extent possible. In addition, the sparse representation application determines and stores difference EPIs that account for variations in the light field. Taken together, the line segments and the difference EPIs compactly store all relevant information that is necessary to reconstruct the full 3D light field and extract an arbitrary input image with a corresponding depth map, or a full 3D point cloud, among other things. This concept also generalizes to higher dimensions. In a 4D light field, for example, the principles of eliminating redundancy and storing a difference volume remain valid.

    Abstract translation: 本公开提供了一种用于产生光场的稀疏表示的方法。 在一种配置中,稀疏表示应用程序接收由多个图像构成的光场,并且采样并存储源自于在极性平面图像(EPI)中的不同位置的一组线段,直到EPI被完全表示并且冗余被消除为止 可能的程度。 此外,稀疏表示应用程序确定并存储解释光场中的变化的差分EPI。 总之,线段和差异EPI紧凑地存储重建完整3D光场所需的所有相关信息,并提取具有相应深度图或完整3D点云的任意输入图像等。 这个概念也概括为更高的维度。 例如,在4D光场中,消除冗余和存储差分体积的原理保持有效。

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