Detection of moving objects is a very important task in autonomous driving systems. After the perception phase, motion planning is typically performed in Bird's Eye View (BEV) space. This would require projection of objects detected on the image plane to top view BEV plane. Such a projection is prone to errors due to lack of depth information and noisy mapping in far away areas. CNNs can leverage the global context in the scene to project better. In this work, we explore end-to-end Moving Object Detection (MOD) on the BEV map directly using monocular images as input. To the best of our knowledge, such a dataset does not exist and we create an extended KITTI-raw dataset consisting of 12.9k images with annotations of moving object masks in BEV space for five classes. The dataset is intended to be used for class agnostic motion cue based object detection and classes are provided as meta-data for better tuning. We design and implement a two-stream RGB and optical flow fusion architecture which outputs motion segmentation directly in BEV space. We compare it with inverse perspective mapping of state-of-the-art motion segmentation predictions on the image plane. We observe a significant improvement of 13% in mIoU using the simple baseline implementation. This demonstrates the ability to directly learn motion segmentation output in BEV space. Qualitative results of our baseline and the dataset annotations can be found in https://sites.google.com/view/bev-modnet.
Ford Greenfield Labs（福特格林菲尔德实验室）；University of California, Berkeley（加州大学伯克利分校）；Ford AV LLC（福特自动驾驶有限责任公司）
We describe a light-weight, weather and lighting invariant, Semantic Bird's Eye View (S-BEV) signature for vision-based vehicle re-localization. A topological map of S-BEV signatures is created during the first traversal of the route, which are used for coarse localization in subsequent route traversal. A fine-grained localizer is then trained to output the global 3-DoF pose of the vehicle using its S-BEV and its coarse localization. We conduct experiments on vKITTI2 virtual dataset and show the potential of the S-BEV to be robust to weather and lighting. We also demonstrate results with 2 veh…
DuoSpaceNet: Leveraging Both Bird's-Eye-View and Perspective View Representations for 3D Object Detection
Carnegie Mellon University（卡内基梅隆大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；University of Washington, Seattle（华盛顿大学西雅图分校）；The Pennsylvania State University（宾夕法尼亚州立大学）
Bird's-Eye-View (BEV) maps have emerged as one of the most powerful representations for scene understanding due to their ability to provide rich spatial context while being easy to interpret and process. Such maps have found use in many real-world tasks that extensively rely on accurate scene segmentation as well as object instance identification in the BEV space for their operation. However, existing segmentation algorithms only predict the semantics in the BEV space, which limits their use in applications where the notion of object instances is also critical. In this work, we present the fir…
Currently, detecting 3D objects in Bird's-Eye-View (BEV) is superior to other 3D detectors for autonomous driving and robotics. However, transforming image features into BEV necessitates special operators to conduct feature sampling. These operators are not supported on many edge devices, bringing extra obstacles when deploying detectors. To address this problem, we revisit the generation of BEV representation and propose detecting objects in perspective BEV -- a new BEV representation that does not require feature sampling. We demonstrate that perspective BEV features can likewise enjoy the b…
SenseTime Research（商汤科技研究院）；Institute of Automation, Chinese Academy of Science (CASIA)（中国科学院自动化研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）
We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detectors are often tied to certain depth pre-trained backbones like VoVNet, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective space supervision. To this end, we propose a two-stage BEV detector, where proposals from the perspective head are fed into the bird's-eye-view head for final predict…
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
Xidian University（西安电子科技大学）；Nanyang Technological University（南洋理工大学）；University of Technology Sydney（悉尼科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
Semantic segmentation in bird's eye view (BEV) is an important task for autonomous driving. Though this task has attracted a large amount of research efforts, it is still challenging to flexibly cope with arbitrary (single or multiple) camera sensors equipped on the autonomous vehicle. In this paper, we present BEVSegFormer, an effective transformer-based method for BEV semantic segmentation from arbitrary camera rigs. Specifically, our method first encodes image features from arbitrary cameras with a shared backbone. These image features are then enhanced by a deformable transformer-based enc…
In this paper, we present BEVerse, a unified framework for 3D perception and prediction based on multi-camera systems. Unlike existing studies focusing on the improvement of single-task approaches, BEVerse features in producing spatio-temporal Birds-Eye-View (BEV) representations from multi-camera videos and jointly reasoning about multiple tasks for vision-centric autonomous driving. Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images. After the ego-motion alignment, the spatio-temporal encode…
Integrating LiDAR and Camera information into Bird's-Eye-View (BEV) has become an essential topic for 3D object detection in autonomous driving. Existing methods mostly adopt an independent dual-branch framework to generate LiDAR and camera BEV, then perform an adaptive modality fusion. Since point clouds provide more accurate localization and geometry information, they could serve as a reliable spatial prior to acquiring relevant semantic information from the images. Therefore, we design a LiDAR-Guided View Transformer (LGVT) to effectively obtain the camera representation in BEV space and th…
Hamilton Institute Maynooth University（汉密尔顿研究所梅努斯大学）；Valeo Vision Systems（法雷奥视觉系统公司）；Maynooth University（梅努斯大学）
Generating a detailed near-field perceptual model of the environment is an important and challenging problem in both self-driving vehicles and autonomous mobile robotics. A Bird Eye View (BEV) map, providing a panoptic representation, is a commonly used approach that provides a simplified 2D representation of the vehicle surroundings with accurate semantic level segmentation for many downstream tasks. Current state-of-the art approaches to generate BEV-maps employ a Convolutional Neural Network (CNN) backbone to create feature-maps which are passed through a spatial transformer to project the …
Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that captures surrounding objects and their spatial locations, along with overall context in the scene. In this work, we focus on bird's eye semantic segmentation, a task that predicts pixel-wise semantic segmentation in BEV from side RGB images. This task is made possible by simulators such as Carla, which allow for cheap data collection, arbitrary camera placements, and supervision in ways otherwise not possible in the real world. There are two main challenges to this task: the view transformation from side…
BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposals
Department of Automobile and IT Convergence, Kookmin University（汽车与IT融合系，韩国釜山大学）；Department of Automotive Engineering, Kookmin University（汽车工程系，韩国釜山大学）；Graduate School of Automobile and Mobility, Kookmin University（汽车与移动研究生院，韩国釜山大学）
SenseTime（商汤科技）；Beijing University of Posts and Telecommunications（北京邮电大学）；The University of Hong Kong（香港大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving. However, most existing BEV solutions either suffer from modest performance or require considerable resources to execute on-vehicle inference. This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing real-time BEV perception on the on-vehicle chips. Towards this goal, we first empirically find that the BEV representation can be sufficiently powerful without expensive view transformation or …
BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging Bird's-Eye-View in Dynamic Scenarios
Tsinghua University（清华大学）；University of Science and Technology Beijing（北京科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Southeast University（东南大学）
Nanjing University（南京大学）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries. To aggregate spatial information, we design spatial cross-attention that each BEV query extracts th…
RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions
Huazhong University of Science and Technology（华中科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
University of Freiburg（弗莱堡大学）；Federal University of Rio Grande（里奥格兰德联邦大学）；University of Technology Nuremberg（纽伦堡工业大学）
Bird's-Eye-View (BEV) semantic maps have become an essential component of automated driving pipelines due to the rich representation they provide for decision-making tasks. However, existing approaches for generating these maps still follow a fully supervised training paradigm and hence rely on large amounts of annotated BEV data. In this work, we address this limitation by proposing the first self-supervised approach for generating a BEV semantic map using a single monocular image from the frontal view (FV). During training, we overcome the need for BEV ground truth annotations by leveraging …
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particularly for generic obstacles/objects, due to appearance and occlusion changes. To tackle this problem, we propose an end-to-end deep learning framework for LIDAR-based flow estimation in bird's eye view (BeV). Our method takes consecutive point cloud pairs as input and produces a 2-D BeV flow grid describing the dynamic state of each cell. The experimental results show that the proposed method not only estimates 2-D BeV…
Autonomous navigation requires scene understanding of the action-space to move or anticipate events. For planner agents moving on the ground plane, such as autonomous vehicles, this translates to scene understanding in the bird's-eye view (BEV). However, the onboard cameras of autonomous cars are customarily mounted horizontally for a better view of the surrounding. In this work, we study scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera. We study three key aspects of this task, image-level understanding, BEV level unde…
Zhejiang University（浙江大学）；DJI（大疆创新）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Shanghai AI Lab（上海人工智能实验室）
Bird's eye view (BEV) representation is a new perception formulation for autonomous driving, which is based on spatial fusion. Further, temporal fusion is also introduced in BEV representation and gains great success. In this work, we propose a new method that unifies both spatial and temporal fusion and merges them into a unified mathematical formulation. The unified fusion could not only provide a new perspective on BEV fusion but also brings new capabilities. With the proposed unified spatial-temporal fusion, our method could support long-range fusion, which is hard to achieve in convention…
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving
Donald Bren School of Information and Computer Sciences, University of California, Irvine（加州大学欧文分校唐纳德·布伦信息与计算机科学学院）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；Nanyang Technological University（南洋理工大学）；Shanghai AI Laboratory（上海人工智能实验室）
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
Shanghai AI Lab（上海人工智能实验室）；SenseTime（商汤科技）；The University of Hong Kong（香港大学）；Chinese University of Hong Kong（香港中文大学）；Huawei Inc.（华为公司）；Beihang University（北京航空航天大学）
BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Polytechnic Institute, Zhejiang University Baidu（浙江大学百度理工学院）；Zhejiang – Singapore Innovation and AI Joint Research Lab（浙新创新与人工智能联合研究实验室）
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion
Stellantis, Opel Automobile GmbH（斯泰兰蒂斯集团欧宝汽车有限公司）；Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；PhiGent Robotics（鉴智机器人）；University of Chinese Academy of Sciences（中国科学院大学）
BEV perception is of great importance in the field of autonomous driving, serving as the cornerstone of planning, controlling, and motion prediction. The quality of the BEV feature highly affects the performance of BEV perception. However, taking the noises in camera parameters and LiDAR scans into consideration, we usually obtain BEV representation with harmful noises. Diffusion models naturally have the ability to denoise noisy samples to the ideal data, which motivates us to utilize the diffusion model to get a better BEV representation. In this work, we propose an end-to-end framework, nam…
OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 Mobility学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
Variational Inference for Bird's Eye View Segmentation in Autonomous Driving
School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子信息工程学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Qualcomm AI Research, an initiative of Qualcomm Technologies, Inc（高通AI研究院，高通技术公司旗下研究机构）；QT Technologies Ireland Limited（QT技术爱尔兰有限公司）；Qualcomm Technologies, Inc（高通技术公司）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Recent works in autonomous driving have widely adopted the bird's-eye-view (BEV) semantic map as an intermediate representation of the world. Online prediction of these BEV maps involves non-trivial operations such as multi-camera data extraction as well as fusion and projection into a common topview grid. This is usually done with error-prone geometric operations (e.g., homography or back-projection from monocular depth estimation) or expensive direct dense mapping between image pixels and pixels in BEV (e.g., with MLP or attention). In this work, we present 'LaRa', an efficient encoder-decod…
