For an unknown characteristic target scene, the laser radar system that uses single-photon detector cannot directly estimate the dwell time of every pixel. Therefore, as the difference of target reflectivity, depth estimation appears inadequate sampling or redundant sampling in the conventional imaging method of maximum likelihood estimation (MLE-CIM). In this work, an adaptive depth imaging method (ADIM) is presented. ADIM is capable to obtain the depth estimation of target and adaptively decide the dwell time of each pixel. The experimental results reveal that ADIM can accurately obtain the 3D depth image of target even at the condition of low signal-to-noise ratio.
This article describes two new algorithms that, when integrated into an existing semi-automatic virtual bone fragment reconstruction system, allow for more accurate anatomic restoration. Furthermore, they spare the user the painstaking task of positioning each fragment in 3D, which can be extremely time consuming and difficult. The virtual interactive environment gives the user capabilities to influence the reconstruction process and that allows idiosyncratic geometric surface reconstruction scenarios. Coarse correspondences specified by the user are refined by a new alignment functional that allows geometric surface variations such as ridges and valleys to more heavily influence the final alignment solution. Integration of these algorithms into the system provides improved reconstruction accuracy, which is critical for increasing the likelihood of satisfactory clinical outcome after the injury.