We describe a new method for comparing frame appearance in a frame-to-model 3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera which is robust to brightness changes caused by auto exposure. It is based on a normalised radiance measure which is invariant to exposure changes and not only robustifies the tracking under changing lighting conditions, but also enables the following exposure compensation perform accurately to allow online building of high dynamic range (HDR) maps. The latter facilitates the frame-to-model tracking to minimise drift as well as better capturing light variation within the scene. Results from experiments with synthetic and real data demonstrate that the method provides both improved tracking and maps with far greater dynamic range of luminosity.
Maxime @cite_7 propose one of the first work in real-time 3-D HDR texture capturing. We follow the same approach of transforming raw RGB into radiance domain and tracking using radiance. It mainly focuses on re-lighting virtual specular objects. The differences between @cite_7 and this paper are two-fold. First, in @cite_7 , a gamma function is adopted to approximate inverse camera response function (CRF). Gamma function may introduce error when radiance is high and the resulting radiance is not directly proportional to scene luminance (Fig. ). Second, in @cite_7 , the exposure is estimated jointly with camera pose, but we find that the shape of error function when tracking using exposure compensated radiance bears shallow global minima even when exposure has been compensated for and, therefore, not as robust as normalized radiance based object function we proposed.(Fig. ) Lastly, mosaic artefacts are clearly visible from the synthetic HDR mode which indicate inadequate exposure estimation (Fig. (d)).
@cite_7
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In many computer vision systems, it is assumed that the image brightness of a point directly reflects the scene radiance of the point. However, the assumption does not hold in most cases due to nonlinear camera response function, exposure changes, and vignetting. The effects of these factors are most visible in image mosaics and textures of 3D models where colors look inconsistent and notable boundaries exist. In this paper, we propose a full radiometric calibration algorithm that includes robust estimation of the radiometric response function, exposures, and vignetting. By decoupling the effect of vignetting from the response function estimation, we approach each process in a manner that is robust to noise and outliers. We verify our algorithm with both synthetic and real data, which shows significant improvement compared to existing methods. We apply our estimation results to radiometrically align images for seamless mosaics and 3D model textures. We also use our method to create high dynamic range (HDR) mosaics that are more representative of the scene than normal mosaics.