FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection

3D object detection with multi-sensors is essential for an accurate and reliable perception system of autonomous driving and robotics. Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm which merely relies on LiDAR point clouds for 3D proposal refinement. Though impressive, the sparsity of point clouds, especially for the points far away, making it difficult for the LiDAR-only refinement module to accurately recognize and locate objects.To address this problem, we propose a novel multi-modality two-stage approach named FusionRCNN, which effectively and efficiently fuses point clouds and camera images in the Regions of Interest(RoI). FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from camera in a unified attention mechanism. Specifically, it first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step; then leverages an intra-modality self-attention to enhance the domain-specific features, following by a well-designed cross-attention to fuse the information from two modalities.FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors.Remarkably, FusionRCNN significantly improves the strong SECOND baseline by 6.14% mAP on Waymo, and outperforms competing two-stage approaches. Code will be released soon at https://github.com/xxlbigbrother/Fusion-RCNN.Comment: 7 pages, 3 figure

FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations to train, and the acquisition is laborious and unaffordable. Therefore, we present FreeMask in this work, which resorts to synthetic images from generative models to ease the burden of both data collection and annotation procedures. Concretely, we first synthesize abundant training images conditioned on the semantic masks provided by realistic datasets. This yields extra well-aligned image-mask training pairs for semantic segmentation models. We surprisingly observe that, solely trained with synthetic images, we already achieve comparable performance with real ones (e.g., 48.3 vs. 48.5 mIoU on ADE20K, and 49.3 vs. 50.5 on COCO-Stuff). Then, we investigate the role of synthetic images by joint training with real images, or pre-training for real images. Meantime, we design a robust filtering principle to suppress incorrectly synthesized regions. In addition, we propose to inequally treat different semantic masks to prioritize those harder ones and sample more corresponding synthetic images for them. As a result, either jointly trained or pre-trained with our filtered and re-sampled synthesized images, segmentation models can be greatly enhanced, e.g., from 48.7 to 52.0 on ADE20K. Code is available at https://github.com/LiheYoung/FreeMask.Comment: Accepted by NeurIPS 202

Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions

Robust 3D perception under corruption has become an essential task for the realm of 3D vision. While current data augmentation techniques usually perform random transformations on all point cloud objects in an offline way and ignore the structure of the samples, resulting in over-or-under enhancement. In this work, we propose an alternative to make sample-adaptive transformations based on the structure of the sample to cope with potential corruption via an auto-augmentation framework, named as AdaptPoint. Specially, we leverage a imitator, consisting of a Deformation Controller and a Mask Controller, respectively in charge of predicting deformation parameters and producing a per-point mask, based on the intrinsic structural information of the input point cloud, and then conduct corruption simulations on top. Then a discriminator is utilized to prevent the generation of excessive corruption that deviates from the original data distribution. In addition, a perception-guidance feedback mechanism is incorporated to guide the generation of samples with appropriate difficulty level. Furthermore, to address the paucity of real-world corrupted point cloud, we also introduce a new dataset ScanObjectNN-C, that exhibits greater similarity to actual data in real-world environments, especially when contrasted with preceding CAD datasets. Experiments show that our method achieves state-of-the-art results on multiple corruption benchmarks, including ModelNet-C, our ScanObjectNN-C, and ShapeNet-C.Comment: Accepted by ICCV2023; code: https://github.com/Roywangj/AdaptPoin

Measurement of line widths and permanent electric dipole moment change of the Ce 4f-5d transition in Y_2SiO_5 for a qubit readout scheme in rare-earth ion based quantum computing

In this work the inhomogeneous (zero-phonon line) and homogeneous line widths, and one projection of the permanent electric dipole moment change for the Ce 4f-5d transition in Y_2SiO_5 were measured in order to investigate the possibility for using Ce as a sensor to detect the hyperfine state of a spatially close-lying Pr or Eu ion. The experiments were carried out on Ce doped or Ce-Pr co-doped single Y_2SiO_5 crystals. The homogeneous line width was measured to be about 3 MHz, which is essentially limited by the excited state lifetime. Based on the line width measurements, the oscillator strength, absorption cross section and saturation intensity were calculated to be about 9*10^-7, 5*10^-19 m^2 and 1*10^7 W/m^2, respectively. One projection of the difference in permanent dipole moment, Delt_miu_Ce, between the ground and excited states of the Ce ion was measured as 6.3 * 10^-30 C*m, which is about 26 times as large as that of Pr ions. The measurements done on Ce ions indicate that the Ce ion is a promising candidate to be used as a probe to read out a single qubit ion state for the quantum computing using rare-earth ions.Comment: 9 figures, 8 page

Third-order nonlinearity in Ge–Sb–Se glasses at mid-infrared wavelengths

International audienceThe optical properties of Ge–Sb–Se glasses have been extensively studied at telecom wavelengths in recent years. However, the understanding of nonlinearity in Ge–Sb–Se glasses at mid-infrared wavelengths still remains limited. In this work, a series of Ge20SbxSe80−x (x = 0, 5, 10) glasses were prepared by conventional melt–quenching method. The absorption spectra and the refractive index of glasses were recorded. The third order nonlinearity, n2, and nonlinear absorption coefficient were measured for Ge–Sb–Se glass samples at the wavelengths of 1550, 2000 and 2500 nm by Z-scan technique, respectively. With the increasing of Sb contents, the linear refractive index of glass increased. Among the three operating wavelengths, all the three glass samples have a highest n2 at 2000 nm. By using the figure of merit (FOM) to evaluate the studied three glasses, the Ge20Sb10Se70 glass shows the greatest potential for mid-IR all optical switching device

Thermodynamic entropy as an indicator for urban sustainability?

As foci of economic activity, resource consumption, and the production of material waste and pollution, cities represent both a major hurdle and yet also a source of great potential for achieving the goal of sustainability. Motivated by the desire to better understand and measure sustainability in quantitative terms we explore the applicability of thermodynamic entropy to urban systems as a tool for evaluating sustainability. Having comprehensively reviewed the application of thermodynamic entropy to urban systems we argue that the role it can hope to play in characterising sustainability is limited. We show that thermodynamic entropy may be considered as a measure of energy efficiency, but must be complimented by other indices to form part of a broader measure of urban sustainability

A simple LC-ESI-MS method for the determination of norvancomycin in rat plasma and application to pharmacokinetic study

A simple and sensitive LC-ESI-MS method for determination of norvancomycin in plasma was developed and validated over the concentration range of 20-2,000 ng/mL. After addition of vancomycin as internal standard (IS), protein precipitation with 5 % trichloroacetic acid was employed for the sample preparation. Chromatographic separation was performed on a Zorbax SB-C18 (100 mm×2.1 mm, 3.5 μm) column with 10:90 (v/v) acetonitrile-0.1 % formic acid as mobile phase. The MS data acquisition was accomplished by selective ions monitoring (SIM) mode with positive electrospray ionization (ESI) interface. The limit of quantification (LOQ) was 20 ng/mL. For inter-day and intra-day tests, the precision (RSD) for the entire validation was less than 12 %. The developed method was successfully applied to pharmacokinetic studies of norvancomycin in rats following single intravenous administration dose of 10 mg/Kg.Colegio de Farmacéuticos de la Provincia de Buenos Aire

What determine the interest rates in China's informal market?

The interest rate is one of the most important factors in farmers' decision-making of borrowing and lending in the informal financial market in China. This paper explores the determinants of the interest rate with microfinance data. Results show that the income disparity, the relationship between borrowers and lenders, the usage of borrowing, and formal credit constraints are important factors affecting interest rates. More importantly, to borrow from those in the higher income hierarchy, farmers have to bear higher interest rates. We attribute this to different social capitals across income groups and higher default risks for the poor. This paper contributes to a better understanding of the informal financial market in rural China and sheds light on the mechanism of higher informal interest rate formation.</p