text: 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.
  @cite_7: Acquiring High Dynamic Range (HDR) light-fields from several images with different exposures (sensor integration periods) has been widely considered for static camera positions. In this paper a new approach is proposed that enables 3D HDR environment maps to be acquired directly from a dynamic set of images in real-time. In particular a method will be proposed to use an RGB-D camera as a dynamic light-field sensor, based on a dense real-time 3D tracking and mapping approach, that avoids the need for a light-probe or the observation of reflective surfaces. The 6dof pose and dense scene structure will be estimated simultaneously with the observed dynamic range so as to compute the radiance map of the scene and fuse a stream of low dynamic range images (LDR) into an HDR image. This will then be used to create an arbitrary number of virtual omni-directional light-probes that will be placed at the positions where virtual augmented objects will be rendered. In addition, a solution is provided for the problem of automatic shutter variations in visual SLAM. Augmented reality results are provided which demonstrate real-time 3D HDR mapping, virtual light-probe synthesis and light source detection for rendering reflective objects with shadows seamlessly with the real video stream in real-time.
summary: 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)).