LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data effectively. However, their fusion methods based on Bird's Eye View (BEV) or voxel format are not accurate. In this paper, we propose a novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points. Except for continuous convolution, we additionally add a Point-Pooling and an Attentive Aggregation to make the fused features more expressive. Moreover, based on the PACF module, we propose a 3D multi-sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks. PI-RCNN employs a segmentation sub-network to extract full-resolution semantic feature maps from images and then fuses the multi-sensor features via powerful PACF module. Beneficial from the effectiveness of the PACF module and the expressive semantic features from the segmentation module, PI-RCNN can improve much in 3D object detection. We demonstrate the effectiveness of the PACF module and PI-RCNN on the KITTI 3D Detection benchmark, and our method can achieve state-of-the-art on the metric of 3D AP.
Anticounterfeiting techniques based on physical unclonable functions exhibit great potential in security protection of extensive commodities from daily necessities to high-end products. Herein, we propose a facile strategy to fabricate an unclonable super micro fingerprint (SMFP) array by introducing in situ grown perovskite crystals for multilevel anticounterfeiting labels. The unclonable features are formed on the basis of the differential transportation of a microscale perovskite precursor droplet during the inkjet printing process, coupled with random crystallization and Ostwald ripening of perovskite crystals originating from their ion crystal property. Furthermore, the unclonable patterns can be readily tailored by tuning in situ crystallization conditions of the perovskite. Three-dimensional height information on the perovskite patterns are introduced into a security label and further transformed into structural color, significantly enhancing the capacity of anticounterfeiting labels. The SMFPs are characterized with tunable multilevel anticounterfeiting properties, including macroscale patterns, microscale unclonable pattern, fluorescent two-dimensional pattens, and colorful three-dimensional information.
Scope of Reproducibility -The following work is a reproducibility report for CLRNet: Cross Layer Refinement Network for Lane Detection [1]. The basic code was made available by the author at this https url. The paper proposes a novel Cross Layer Refinement Network to utilize both high and low level features for lane detection. The authors assert that the proposed technique sets the new state-of-the-art on three lane-detection benchmarks.Methodology -The proposed model employs a two-stage approach to lane detection. Initially, coarse lane detection is achieved through the extraction of high-level semantic features. This is followed by refinement of the output based on low-level features, aimed at enhancing the localization accuracy of the model. The authors' code was used to benchmark the claims. Some further experiments were investigated thereafter. Kaggle, a free-to-use platform for deep learning experiments, was used to train these models. We have reproduced the code base in Pytorch Lightning and found consistent results across the board. Results -The central claims presented by the authors were subject to reproduction and verification. The validity of the claims was evaluated using two out of the three datasets referenced in the original paper. The results obtained from the CULane dataset showed close agreement with the original findings, with deviations of less than 1% on most of the metrics. This suggests the reproducibility and reliability of the claims made by the authors. However, in experiments on the TuSimple dataset, substantial disparities were noted between our results and those reported in the original paper. The probable causes of these inconsistencies are discussed in the study.What was easy -Obtaining the proposed results on the CULane dataset was readily achievable. The codebase provided by the authors was well-documented and functional. Owing to the modularity of the code, further experiments could be run with minimal changes overall. Porting the codebase to PyTorch Lightning was also facile.What was difficult -Using the LLAMAS dataset proved to be a challenge for resource constrained students owing to its size. We were eventually unable to set up experimentation on that dataset. Limited computational resources proved to be a challenge even for the other datasets, with each epoch taking over 2 hours on CULane. Total training time
Optical security labels play a significant role in protecting both our wealth and health. However, simultaneously meeting the requirements including low-cost fabrication, easy detection, and high-level security is still challenging for security labels. Here, we design an unclonable anti-counterfeiting system with triple-level security by using the inkjet printing technique, which can be authenticated by naked eyes, a portable microscope, and a fluorescence microscope. These labels are achieved by printing microscale quantum dot (QD) ink droplets on premodified substrates with random-distributed glass microspheres. Due to the unique capillary action induced by the glass microspheres, QDs in the ink droplets are deposited around the microspheres, forming microscale multicircular patterns. Multiple pinning of QDs at the three-phase contact lines appears during the evaporation of the droplet, resulting in the formation of a nanoscale labyrinthine pattern around the microspheres. The nanoscale labyrinth pattern and the microscale multicircular microsphere array, together with the printed macroscopic image, constitute a triple-level progressive anti-counterfeiting system. Moreover, the system is compatible with an artificial intelligence-based identification strategy that allows rapid identification and verification of the unclonable security labels.
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