Abstract

The 3D Image warping algorithm by McMillan and Bishop uses regular single-layered depth images (which are called reference images) as the initial input. Tears or gaps may appear in the output when the previously occluded areas are exposed in the new viewing position. Many reference images that are taken from different positions can be used to attenuate the occlusion problems but the rendering cost increases with the number of reference images. Also, combining the multiple reference images and eliminating the redundant information is a non-trivial problem. Recently, the Layered Depth Image (LDI) was proposed by Shade et al. to merge multiple reference images under a single center of projection. It tackles the occlusion problems by keeping multiple depth pixels per pixel location, while still maintaining the simplicity of warping a single reference image. However, it does not consider the issue of sampling rate. We present the LDI tree, which combines a hierarchical space partition scheme with the concept of LDI. It preserves the sampling rates of the reference images by adaptively selecting an LDI in the LDI tree for each pixel. While rendering from the LDI tree, we only have to traverse the LDI tree to the levels that are comparable to the sampling rate of the output image. We also present the progressive refinement feature and a "gap filling" algorithm by pre-filtering the LDI tree. We show that the amount of memory required is of the same order as the 2D reference images. This also bounds the complexity of rendering time to be less than directly rendering from all reference images. 1.

Citations