Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving

Sharing collective perception messages (CPM) between vehicles is investigated to decrease occlusions so as to improve the perception accuracy and safety of autonomous driving. However, highly accurate data sharing and low communication overhead is a big challenge for collective perception, especially when real-time communication is required among connected and automated vehicles. In this paper, we propose an efficient and effective keypoints-based deep feature fusion framework built on the 3D object detector PV-RCNN, called Fusion PV-RCNN (FPV-RCNN for short), for collective perception. We introduce a high-performance bounding box proposal matching module and a keypoints selection strategy to compress the CPM size and solve the multi-vehicle data fusion problem. Besides, we also propose an effective localization error correction module based on the maximum consensus principle to increase the robustness of the data fusion. Compared to a bird's-eye view (BEV) keypoints feature fusion, FPV-RCNN achieves improved detection accuracy by about 9% at a high evaluation criterion (IoU 0.7) on the synthetic dataset COMAP dedicated to collective perception. In addition, its performance is comparable to two raw data fusion baselines that have no data loss in sharing. Moreover, our method also significantly decreases the CPM size to less than 0.3 KB, and is thus about 50 times smaller than the BEV feature map sharing used in previous works. Even with further decreased CPM feature channels, i.e., from 128 to 32, the detection performance does not show apparent drops. The code of our method is available at https://github.com/YuanYunshuang/FPV_RCNN

Effects of bleaching agents on dental restorative materials: A review of the literature and recommendation to dental practitioners and researchers

AbstractIn recent years, there has been an increased demand for improvement in the appearance of natural teeth. The conservative technique of tooth bleaching has gained attention and acceptance from both patients and clinicians. Despite increased popularity, there is controversy surrounding the adverse effects of bleaching on dental restorative materials. This article reviews the effects of bleaching agents on major categories of dental restorative materials and provides evidence-based recommendations to the clinicians and researchers. Current literature reveal that bleaching might have a detrimental effect on restorative materials. However, because of the variability in experimental design, there is a lack of consensus concerning the bleaching effects on restorative materials. A standardized and reproducible guideline for assessment of bleaching effects on restorative materials needs to be established and verified by future studies