Error Analysis for Image-Based Rendering With Depth Information

Abstract

We propose a new approach to quantitatively analyze the rendering quality of image-based rendering (IBR) algorithms with depth information. The resulting error bounds for synthesized views depend on IBR configurations including the depth and intensity estimate errors, the scene geometry and texture, the number of actual cameras, their positions and resolution. Specifically, the IBR error is bounded by the summation of three terms, highlighting the impact of using multiple actual cameras, the impact of the noise level at the actual cameras, and the impact of the depth accuracy. We also quantify the impact of occlusions and intensity discontinuities. The proposed methodology is applicable to a large class of common IBR algorithms and can be applied locally. Experiments with synthetic and real scenes show that the developed error bounds accurately characterize the rendering errors. In particular, the error bounds correctly characterize the decay rates of synthesized views' mean absolute errors as O(lambda(-1)) and O(lambda(-2)), where lambda is the local density of actual samples, for 2-D and 3-D scenes, respectively. Finally, we discuss the implications of the proposed analysis on camera placement, budget allocation, and bit allocation.