Influence of contact points on the performance of caries detection methods in approximal surfaces of primary molars: an in vivo study

This in vivo study aimed to evaluate the influence of contact points on the approximal caries detection in primary molars, by comparing the performance of the DIAGNOdent pen and visual-tactile examination after tooth separation to bitewing radiography (BW). A total of 112 children were examined and 33 children were selected. In three periods (a, b, and c), 209 approximal surfaces were examined: (a) examiner 1 performed visual-tactile examination using the Nyvad criteria (EX1); examiner 2 used DIAGNOdent pen (LF1) and took BW; (b) 1 week later, after tooth separation, examiner 1 performed the second visual-tactile examination (EX2) and examiner 2 used DIAGNOdent again (LF2); (c) after tooth exfoliation, surfaces were directly examined using DIAGNOdent (LF3). Teeth were examined by computed microtomography as a reference standard. Analyses were based on diagnostic thresholds: D1: D 0 = health, D 1 –D 4 = disease; D2: D 0 , D 1 = health, D 2 –D 4 = disease; D3: D 0 –D 2 = health, D 3 , D 4 = disease. At D1, the highest sensitivity/specificity were observed for EX1 (1.00)/LF3 (0.68), respectively. At D2, the highest sensitivity/ specificity were observed for LF3 (0.69)/BW (1.00), respectively. At D3, the highest sensitivity/specificity were observed for LF3 (0.78)/EX1, EX2 and BW (1.00). EX1 showed higher accuracy values than LF1, and EX2 showed similar values to LF2. We concluded that the visual-tactile examination showed better results in detecting sound surfaces and approximal caries lesions without tooth separation. However, the effectiveness of approximal caries lesion detection of both methods was increased by the absence of contact points. Therefore, regardless of the method of detection, orthodontic separating elastics should be used as a complementary tool for the diagnosis of approximal noncavitated lesions in primary molars.

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

Regularizing Image Reconstruction for Gradient-Domain Rendering with Feature Patches

We present a novel algorithm to reconstruct high-quality images from sampled pixels and gradients in gradient-domain rendering. Our approach extends screened Poisson reconstruction by adding additional regularization constraints. Our key idea is to exploit local patches in feature images, which contain per-pixels normals, textures, position, etc., to formulate these constraints. We describe a GPU implementation of our approach that runs on the order of seconds on megapixel images. We demonstrate a significant improvement in image quality over screened Poisson reconstruction under the L1 norm. Because we adapt the regularization constraints to the noise level in the input, our algorithm is consistent and converges to the ground truth.

Denoising Your Monte Carlo Renders: Recent Advances in Image Space-Adaptive Sampling and Reconstruction

With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a need for effective, practical noise-reduction techniques that are applicable to a wide range of rendering effects and easily integrated into existing production pipelines. This course surveys recent advances in image-space adaptive sampling and reconstruction algorithms for noise reduction, which have proven very effective at reducing the computational cost of Monte Carlo techniques in practice. These approaches leverage advanced image-filtering techniques with statistical methods for error estimation. They are attractive because they can be integrated easily into conventional Monte Carlo rendering frameworks, they are applicable to most rendering effects, and their computational overhead is modest.