https://github.com/fundamentalvision/BEVFormergithub.com/fundamentalvision/BEVFormer 摘要 3D视觉感知任务,包括基于多摄像头图像的3D检测和地图分割,对于自动驾驶系统至关重要。 在这项工作中,我们提出了一个名为BEVFormer的新框架,该框架通过时空变换器学习统一的BEV表示,以支持多个自动驾驶感知任务。简而言之,...
对于nuScenes的实验,BEV queries默认尺寸是200\times 200,感知范围(perception ranges)是[-51.2m, 51.2m],所以BEV网格分辨率s为0.512m。作者对BEV queris采用的可学习的位置编码(positional embedding)。BEV encoder层包含6个encoder layer,并且持续细化(refine)每层中的BEV queries(第一层encoder layer的输入作为第二...
论文题目:BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers 参考与前言 arXiv 地址: BEVFormer: Learning B
报告主题：《BEVFormer：基于时空融合的BEV感知》内容概要：BEV 感知通过将多视角相机进行前融合，从而能够在统一的特征空间进行 3D 感知，摆脱了传统方法对于后融合的依赖。近两年，包括 BEVFormer 在内的一系列 BEV 感知方法通过引入时序信息，优化深度估计等技术，大幅度提高了纯视觉自动驾驶感知的精度。本次我将简要...
BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers link 时间:22.07 机构:Nanjing University && Shanghai AI Laboratory TL;DR 利用Transformer的Attention机制融合时空特征信息,在nuScenes测试集上达到SOTA精度,同时在速度估计以及可见度低路况也有明显精度提升。
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 s
如图1所示,BEVFormer v2主要由五个组件组成:图像主干、透视3D检测头、空间编码器、改进的时间编码器和BEV检测头。与原始BEVFormer相比,除了空间编码器外,对所有组件进行了更改。具体而言,BEVFormer v2中使用的所有图像主干均未使用任何自动驾驶数据集或深度估计数据集进行预训练。引入透视3D检测头以促进2D图像主干的适配...
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 ...
BEVFormer: Learning Bird's-Eye-View Representation From LiDAR-Camera Via Spatiotemporal Transformers 来自 IEEEXplore 喜欢 0 阅读量: 8 作者:Z Li,W Wang,H Li,E Xie,C Sima,T Lu,Q Yu,J Dai 摘要: Multi-modality fusion strategy is currently the de-facto most competitive solution for...
Bird's-Eye View Semantic SegmentationLyft Level 5BEVFormer(ResNet-50)IoU vehicle - 224x480 - Long43.2# 6 Compare IoU vehicle - 224x480 - Short68.8# 6 Compare 3D Object DetectionnuScenesBEVFormerNDS0.57# 216 Compare mAP0.48# 208 Compare ...