PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module

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 Birds 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.Comment: 8 pages, 5 figure

Lidar Point Cloud Guided Monocular 3D Object Detection

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird's-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG.Comment: ECCV 202

Sub-5 nm single crystalline organic p-n heterojunctions.

The cornerstones of emerging high-performance organic photovoltaic devices are bulk heterojunctions, which usually contain both structure disorders and bicontinuous interpenetrating grain boundaries with interfacial defects. This feature complicates fundamental understanding of their working mechanism. Highly-ordered crystalline organic p-n heterojunctions with well-defined interface and tailored layer thickness, are highly desirable to understand the nature of organic heterojunctions. However, direct growth of such a crystalline organic p-n heterojunction remains a huge challenge. In this work, we report a design rationale to fabricate monolayer molecular crystals based p-n heterojunctions. In an organic field-effect transistor configuration, we achieved a well-balanced ambipolar charge transport, comparable to single component monolayer molecular crystals devices, demonstrating the high-quality interface in the heterojunctions. In an organic solar cell device based on the p-n junction, we show the device exhibits gate-tunable open-circuit voltage up to 1.04 V, a record-high value in organic single crystalline photovoltaics

The effect of Y, Ce and Gd on texture, recrystallization and mechanical property of Mg–Zn alloys

The effect of Gd, Ce and Y elements on texture, recrystallization and mechanical properties of Mg–1.5Zn alloys was investigated. The results show that the addition of Gd, Ce and Y elements in Mg–1.5Zn alloy, which rolled at 450 °C and subsequently annealed at 350 °C for 1h, can effectively weaken and modify the basal texture, characterized by the splitting basal pole toward to transverse direction, leading to the yield and tensile strength, the highest along the rolling direction and the lowest along the transverse direction. Besides, the unique basal texture contributes to the significant improvement of elongation at room temperature. Electron back scattering diffraction (EBSD) analysis indicated that the non-basal texture in Mg–1.5Zn–0.2RE alloys can be attributed to obstructive effect of static recrystallization and the non-basal orientation grains nucleation near pre-existing grain boundaries during annealing. Specially, the Mg–1.5Zn–0.2Gd sheet exhibits much excellent plasticity with the elongation of 27% than Mg–1.5Zn–0.2Ce and Mg–1.5Zn–0.2Y alloys, resulting from the less and smaller second phase of MgZnGd

Promotional Effect of La in the Three-Way Catalysis of La-Loaded Al2O3-Supported Pd Catalysts (Pd/La/Al2O3)

La-loaded Al2O3 (La/Al2O3) is a practical support for three-way catalysis (TWC) reactions. Although it has been reported that the addition of La to Al2O3 results in improved thermal stability to retain high specific surface areas, its effect on the catalytic reduction of NOx (DeNO(x)) has not been studied systematically. Herein, we describe the role of La in La/Al2O3-supported Pd catalysts (Pd/La/Al2O3) for TWC reactions. For that purpose, we employed various in situ spectroscopic studies, including infrared (IR), X-ray absorption fine structure (XAFS), and near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS) in combination with density functional theory (DFT) calculations. The obtained results revealed that Pd-0 species supported on La/Al2O3 are more electron-deficient compared to those on pristine Al2O3 without La(Pd/Al2O3). Kinetic studies using powdered catalysts revealed that the addition of La suppresses the poisoning effect by CO during the DeNO(x) reactions. In addition to the catalytic tests with powdered catalysts, monolithic honeycomb forms of the catalysts were prepared and employed for TWC reactions, which showed that Pd/La/Al2O3 exhibits higher DeNO(x) activity than Pd/Al2O3. In this study, we also reexamined the effective loading amount of La, which has traditionally been similar to 3-5 wt % of La for TWC processes in order to retain the high specific surface area of the La/Al2O3 supports. Our investigations showed that an increased La loading (15 wt %) is even more effective for the DeNO(x) reactions tested in this study due to the higher reactivity toward NO and the greater suppression of the poisoning effect of CO. The developed catalyst Pd/La(15)/Al2O3 has also been tested in a commercial vehicle and has been evaluated on a practical driving mode test cycle (LA-4; city cycle of U.S. Federal and California), where it showed a better catalytic performance than the conventionally used Pd/La(3-5)/Al2O3 catalysts. Our study suggests that the loading amount of La in Pd/La/Al2O3 catalysts needs to be adjusted depending on the application systems, considering not only the support stability (surface areas) but also the promotional effect in the TWC process