Bird's Eye View Research Articles

Famous places along the Keifuku Electric Railway line in the prewar period as depicted in Hatsusaburo Yoshida's style bird's eye view map

Famous places along the Keifuku Electric Railway line in the prewar period as depicted in Hatsusaburo Yoshida's style bird's eye view map

BEVEFNet: A Multiple Object Tracking Model Based on LiDAR-Camera Fusion

As a crucial task in the field of computer vision, object tracking models are widely used in various application domains, such as autonomous driving. However, existing multiple object tracking methods still face challenges in accurately and efficiently tracking moving multi-targets in real time. This paper presents BEVEFNet, a camera-LiDAR multi-target tracking model based on multistage fusion, which effectively utilizes the semantic information from optical images and the spatial and geometric information from LiDAR data to unify multi-modal features in a shared Bird’s Eye View(BEV) representation space. By leveraging LiDAR data to complement optical images, multi-level fusion is achieved at both the feature and decision levels. The proposed efficient sparse 3D feature extraction network significantly enhances the speed of multiple object tracking by incorporating sparse convolution. Experiments conducted on the nuSences dataset demonstrate that BEVEFNet achieves an AMOTA of 69.7, improving the accuracy of multiple object tracking.

Operationalizing AI/ML in Future Networks: A Bird's Eye View from the System Perspective

Operationalizing AI/ML in Future Networks: A Bird's Eye View from the System Perspective

FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems

FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems

The Effect of Postsecondary Educational Institutions on Local Economies: A Bird's-Eye View

The Effect of Postsecondary Educational Institutions on Local Economies: A Bird's-Eye View

Fully Sparse Fusion for 3D Object Detection.

Currently prevalent multi-modal 3D detection methods rely on dense detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not scalable for long-range detection. Recently, LiDAR-only fully sparse architecture has been gaining attention for its high efficiency in long-range perception. In this paper, we study how to develop a multi-modal fully sparse detector. Specifically, our proposed detector integrates the well-studied 2D instance segmentation into the LiDAR side, which is parallel to the 3D instance segmentation part in the LiDAR-only baseline. The proposed instance-based fusion framework maintains full sparsity while overcoming the constraints associated with the LiDAR-only fully sparse detector. Our framework showcases state-of-the-art performance on the widely used nuScenes dataset, Waymo Open Dataset, and the long-range Argoverse 2 dataset. Notably, the inference speed of our proposed method under the long-range perception setting is 2.7× faster than that of other state-of-the-art multimodal 3D detection methods. Code is released at https://github.com/BraveGroup/FullySparseFusion.

Divide and Conquer: Improving Multi-Camera 3D Perception With 2D Semantic-Depth Priors and Input-Dependent Queries.

3D perception tasks, such as 3D object detection and Bird's-Eye-View (BEV) segmentation using multi-camera images, have drawn significant attention recently. Despite the fact that accurately estimating both semantic and 3D scene layouts are crucial for this task, existing techniques often neglect the synergistic effects of semantic and depth cues, leading to the occurrence of classification and position estimation errors. Additionally, the input-independent nature of initial queries also limits the learning capacity of Transformer-based models. To tackle these challenges, we propose an input-aware Transformer framework that leverages Semantics and Depth as priors (named SDTR). Our approach involves the use of an S-D Encoder that explicitly models semantic and depth priors, thereby disentangling the learning process of object categorization and position estimation. Moreover, we introduce a Prior-guided Query Builder that incorporates the semantic prior into the initial queries of the Transformer, resulting in more effective input-aware queries. Extensive experiments on the nuScenes and Lyft benchmarks demonstrate the state-of-the-art performance of our method in both 3D object detection and BEV segmentation tasks.

CI3D: Context Interaction for Dynamic Objects and Static Map Elements in 3D Driving Scenes.

Multi-view 3D visual perception including 3D object detection and Birds'-eye-view (BEV) map segmentation is essential for autonomous driving. However, there has been little discussion about 3D context attention between dynamic objects and static elements with multi-view camera inputs, due to the challenging nature of recovering the 3D spatial information from images and performing effective 3D context interaction. 3D context information is expected to provide more cues to enhance 3D visual perception for autonomous driving. We thus propose a new transformer-based framework named CI3D in an attempt to implicitly model 3D context interaction between dynamic objects and static map elements. To achieve this, we use dynamic object queries and static map queries to gather information from multi-view image features, which are represented sparsely in 3D space. Moreover, a dynamic 3D position encoder is utilized to precisely generate queries' positional embeddings. With accurate positional embeddings, the queries effectively aggregate 3D context information via a multi-head attention mechanism to model 3D context interaction. We further reveal that sparse supervision signals from the limited number of queries result in the issue of rough and vague image features. To overcome this challenge, we introduce a panoptic segmentation head as an auxiliary task and a 3D-to-2D deformable cross-attention module, greatly enhancing the robustness of spatial feature learning and sampling. Our approach has been extensively evaluated on two large-scale datasets, nuScenes and Waymo, and significantly outperforms the baseline method on both benchmarks.

Bird eye view of saphenous incompetence from a veteran of angiology

Conversation with Claudio Allegra, a "veteran" of angiology, a protagonist of all the seasons of Phlebology, former President of the International Union of Phlebology (IUP) and of the International Union of Angiology (IUA). A chat with Stefano Ricci.

KPDet: Keypoint-based 3D object detection with Parametric Radius Learning

3D object detection requires accurate recognition of object category, size, rotation angle, and location in 3D space. Currently, many 3D object detection methods rely on the compact Bird’s Eye View (BEV) or point-wise representations to generate proposals. However, these proposal generation paradigms neglect the spatial distribution of objects, which causes difficulty in estimating the centers of objects. In this paper, we propose a keypoint-based 3D object detector, KPDet, which employs the keypoints in the neighborhood of object centers as the representation for proposal generation. The proposed KPDet first uses voxel-keypoint mapping to aggregate informative features on the subsampled keypoints from voxel-wise features, then calibrates the misalignment between the keypoints and object centers through the Object-aware Feature Pooling (OFP) module. These aligned keypoints with their corresponding features are applied to generate proposals. Since the keypoints are essential components, we further present the Structural Point Abstraction (SPA) module, which captures the anisotropic features of keypoints via constructed structural points to enhance the geometric information. In addition, based on the well-studied multi-task learning framework, we also propose a Parametric Radius Learning (PRL) strategy to adjust the sampling radius of the point-based feature aggregation process during the training procedure. Extensive experiments on the KITI and Waymo Open Dataset show that KPDet could achieve promising results compared with previous works.

Review and analysis of empirical articles published in TESOL Quarterly over its lifespan

We report the results of a bibliometric study of 696 empirical articles (EAs) published in TESOL Quarterly (TQ) over its lifespan (1967-2019). We report overall and periodic reviews (1967-1979, 1980-1989, 1990-1999, 2000-2009, 2010-2019) concerning the following themes: (1) contexts and participants, (2) research foci and theoretical orientations, and (3) research methodology and data sources. A typical article was written by a single author addressing a learning/teaching English issue related to undergraduates in US universities. The most common research foci were instruction, learning, and assessment. A quarter of the articles did not have a specifiable theoretical orientation, and for those that had, the main theoretical orientations were linguistic/scientific, linguistic/cognitive, and social. The most frequently used research methodologies were quantitative, qualitative, and eclectic, and the top three data sources used by researchers were elicitation, multiple sources, and observation. Based on the findings, we make suggestions for future research in TESOL. Overall, the present review and analysis of published EAs give readers a birds-eye view of the research gravity in TQ over the last 52 years.

Optimizing the doses of cancer drugs after usual dose finding.

Since the middle of the 20th century, oncology's dose-finding paradigm has been oriented toward identifying a drug's maximum tolerated dose, which is then carried forward into phase 2 and 3 trials and clinical practice. For most modern precision medicines, however, maximum tolerated dose is far greater than the minimum dose needed to achieve maximal benefit, leading to unnecessary side effects. Regulatory change may decrease maximum tolerated dose's predominance by enforcing dose optimization of new drugs. Dozens of already approved cancer drugs require re-evaluation, however, introducing a new methodologic and ethical challenge in cancer clinical trials. In this article, we assess the history and current landscape of cancer drug dose finding. We provide a set of strategic priorities for postapproval dose optimization trials of the future. We discuss ethical considerations for postapproval dose optimization trial design and review three major design strategies for these unique trials that would both adhere to ethical standards and benefit patients and funders. We close with a discussion of financial and reporting considerations in the realm of dose optimization. Taken together, we provide a comprehensive, bird's eye view of the postapproval dose optimization trial landscape and offer our thoughts on the next steps required of methodologies and regulatory and funding regimes.

A Birds Eye View on Solid Lipid Nanoparticles and Applications in Drug Delivery System

Solid lipid nanoparticles (SLN) might provide fresh opportunities for treating challenging ailments. The SLN were established in the 1990s to replace emulsions and liposomes, in addition to polymeric nanoparticles (NP) as carrier systems. SLN are wet cohesive dispersions with solid biodegradable lipids as their matrices. Drug delivery techniques called SLN use both liquid and solid lipids as their primary matrices. It was demonstrated that SLNs have several benefits over conventional carriers for drug therapy, a longer half-life, tissue-targeted administration, higher permeability, enhanced bioavailability, enhanced solubility, as well as the capacity to enhance storage stability. Because of their exclusive size-dependent characteristics as well as their capability towards incorporating drugs, SLN’s currently a possibility towards designing promising pharmacological prototypes for drug transport as well as targeting. The objective of tailored as well as monitored drug delivery is now unsettling researchers’ interests across the globe, and can be accomplished through the support of SLNs. The present investigation emphasizes SLNs’ numerous characteristics as well as development and evaluation processes, formulation factors, delivery routes, surface changes, toxicity, and biomedical applications

Comparative Study on BEV Vision and LiDAR Point Cloud Data Fusion Methods

With the gradual maturity of autonomous driving technology, efficient fusion and processing of multimodal sensor data has become an important research direction. This study mainly explores the strategy of integrating BEV (Bird's Eye View) vision with LiDAR point cloud data. We evaluated the performance and applicability of each of the three main data fusion methods through in-depth comparison: early fusion, mid-term fusion, and late fusion. First of all, we summarize the working principle and data characteristics of BEV vision and LiDAR, and emphasize their key roles in auto drive system. Then, the theoretical basis and implementation methods of the three fusion strategies were described in detail. The experimental results show that different fusion strategies exhibit their own advantages in different application scenarios and requirements. For example, early fusion performs well in high-precision tasks, but has a high demand for computing resources. And mid-term fusion is more suitable in scenarios with high real-time requirements. Overall, this study provides in-depth insights and practical suggestions on the fusion of BEV vision and LiDAR data in the field of autonomous driving, laying a solid foundation for future research and applications.

Predicting Maps Using In-Vehicle Cameras for Data-Driven Intelligent Transport

Bird’s eye view (BEV) semantic maps have evolved into a crucial element of urban intelligent traffic management and monitoring, offering invaluable visual and significant data representations for informed intelligent city decision making. Nevertheless, current methodologies continue underutilizing the temporal information embedded within dynamic frames throughout the BEV feature transformation process. This limitation results in decreased accuracy when mapping high-speed moving objects, particularly in capturing their shape and dynamic trajectory. A framework is proposed for cross-view semantic segmentation to address this challenge, leveraging simulated environments as a starting point before applying it to real-life urban imaginative transportation scenarios. The view converter module is thoughtfully designed to collate information from multiple initial view observations captured from various angles and modes. This module outputs a top-down view semantic graph characterized by its object space layout to preserve beneficial temporal information in BEV transformation. The NuScenes dataset is used to evaluate model effectiveness. A novel application is also devised that harnesses transformer networks to map images and video sequences into top-down or comprehensive bird’s-eye views. By combining physics-based and constraint-based formulations and conducting ablation studies, the approach has been substantiated, highlighting the significance of context above and below a given point in generating these maps. This innovative method has been thoroughly validated on the NuScenes dataset. Notably, it has yielded state-of-the-art instantaneous mapping results, with particular benefits observed for smaller dynamic category displays. The experimental findings include comparing axial attention with the state-of-the-art (SOTA) model, demonstrating the performance enhancement associated with temporal awareness.

Using AI-Enabled Divergence and Convergence Patterns as a Quantitative Artifact in Design Education

Abstract Design education has traditionally relied heavily on physical integration as it involves a lot of hands-on work, group critiques, and collaborative projects, but the COVID-19 pandemic has forced many institutions to adapt to remote teaching and learning environments. This has created challenges for design educators who have had to find ways to evaluate students' progress in the absence of in-person interactions. In this paper, we are proposing a dashboard visualisation approach that helps educators to monitor the progression of the entire class of students using Artificial Intelligence (AI) by tracking a time-based evolution of a design statement. This approach uses various Natural Language Processing (NLP) models to produce stock-like charts which represent students' and student groups' progression through a series of divergence and convergence phases. These charts become a form of Design Artifact that allows educator(s) to gain a birds-eye view of the class and react to groups that may require assistance; at the same time it becomes a qualitative means of evaluation and comparison across students and groups. Towards the end, this paper also showcases a web-based platform that is publicly available using such methodology, a case study that applied so methodology and recommendation of future works possible.

Political studies of automated governing: A bird's eye (re)view

AbstractIn this paper, we develop an approach for analyzing the increasingly important strand of research that deals with automated systems of governing. Such systems, which figure prominently in public policy and regulation, are designed to utilize the rapid advancement in computer technology, like artificial intelligence, with the purpose of governing something or someone. Drawing on a large sample of articles we present a comprehensive analysis of scholarly works where these systems are studied as political, rather than neutral, instruments of governing. We find that the current state of the art articulates the politics of automated systems of governing in three ways. Namely, as part of ontological, epistemological and ideological questions. We conclude that future research should investigate the complex forms of marketization nested in these systems, that it should move from theoretical examples to detailed empirical studies and that political science should get more involved with the issue.

Cosmetic facial surgery: a bird's eye view

Surgical procedures that rejuvenate and/or harmonize the face are known for their long-lasting and clinically relevant positive effects on the psychological well-being and quality of life of the client. Despite the popularity of cosmetic injectables, facelift surgery remains the most powerful and durable treatment for restoring aging symptoms, especially in the female face. Skin correction of the upper eyelids is the most commonly performed aesthetic facial surgery. This article provides an overview of the most important cosmetic surgical procedures for the face, focusing on the facelift, neck lift, forehead lift, upper and lower eyelid correction, subnasal lip lift and rhinoplasty. The indications, contraindications, treatment techniques, advantages and disadvantages are explained. Due to the increasing demand for cosmetic treatments, it is important that dentists and oral and maxillofacial surgeons have knowledge of these procedures in order to provide optimal information, referral or treatment when desired.

CSI clinical practice guidelines for dyslipidemia management: Executive summary

Dyslipidemias are the most important coronary artery disease (CAD) risk factor. Proper management of dyslipidemia is crucial to control the epidemic of premature CAD in India. Cardiological Society of India strived to develop consensus-based guidelines for better lipid management for CAD prevention and treatment. The executive summary provides a bird's eye-view of the ‘CSI: Clinical Practice Guidelines for Dyslipidemia Management’ published in this issue of the Indian Heart Journal. The summary is focused on the busy clinician and encourages evidence-based management of patients and high-risk individuals. The summary has serialized various aspects of lipid management including epidemiology and categorization of CAD risk. The focus is on management of specific dyslipidemias relevant to India-raised low density lipoprotein (LDL) cholesterol, non-high density lipoprotein cholesterol (non-HDL-C), apolipoproteins, triglycerides and lipoprotein(a). Drug therapies for lipid lowering (statins, non-statin drugs and other pharmaceutical agents) and lifestyle management (dietary interventions, physical activity and yoga) are summarized. Management of dyslipidemias in oft-neglected patient phenotypes-the elderly, young and children, and patients with comorbidities-stroke, peripheral arterial disease, kidney failure, posttransplant, HIV (Human immunodeficiency virus), Covid-19 and familial hypercholesterolemia is also presented. This consensus statement is based on major international guidelines (mainly European) and expert opinion of lipid management leaders from India with focus on the dictum: earlier the better, lower the better, longer the better and together the better. These consensus guidelines cannot replace the individual clinician judgement who remains the sole arbiter in management of the patient.

MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation With Bird's Eye View Based Appearance and Motion Features

MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation With Bird's Eye View Based Appearance and Motion Features