BEVDet4D框架:基于BEVDet,包含图像视图编码器image-view encoder、视图变换器view transformer、BEV编码器BEV encoder和特定任务头task-specific head。 利用时间线索融合特征:通过空间对齐(spatial alignment)操作和拼接操作将保留的特征(generated by the view transformer)与
在验证可行性时,BEVDet数据处理策略和参数数量被设置为接近image-view-based 3D object detector(FCOS3D, PGD),但在训练时出现过拟合问题。BEVDet在BEV空间中过拟合的部分原因是训练数据的不足。 在没有BEV encoder的情况下,对image view space使用LSS的数据增强策略实现正则化效果有积极作用;而在有BEV encoder的情...
当对输入图像应用数据增强策略(\mathbf p^{'}_{image}=\mathbf A \mathbf p_{image})时,则应该在view-transformer中施加一个逆变换\mathbf A^{-1},以保持BEV空间中的特征和目标的空间一致性: \begin{align} \mathbf p^{'}_{camera} & = \mathbf I^{-1}(\mathbf A^{-1} \mathbf p^{'}_{ima...
Training Parameters: 与BEVDet基本一致,采用AdamW优化器,用梯度裁剪,学习率为2e-4,batch_size为64,用8个NVIDIA GeForce RTX 309GPUs上训练。采用循环策略,前40%epochs中,学习率从2e-4到1e-3线性增长,剩下的epochs学习率从1e-3线性衰减到0,默认情况下,共训练20个epochs。 Data Processing: 所有的数据处理过程...
Collaborative object detection by multiple cameras can make up for the limitation of insufficient field of view of a single camera, and through the rich information obtained from multiple perspectives, object detection and other tasks can be better completed. For the sake of detect moving object in...
Recently, some large-scale benchmark [1, 44] have been released with more data and multiple views, offering new perspectives toward the paradigm development in multi-camera 3D object detection. Based on these benchmarks, some multi-camera 3D object detection paradigms have been developed with ...
High-Performance Multi-Camera 3D Object Detection in Bird-Eye-View 自主驾驶感知周围环境进行决策,这是视觉感知中最复杂的场景之一。在解决2D目标检测任务方面的成功创新激励领域寻求一种优雅、可行和可扩展的范式,从根本上推动该领域的性能边界。为此,论文贡献了BEVDet范式,BEVDet在鸟瞰图(BEV)中执行3D对象检测,其...
3D perception based on the representations learned from multi-camera bird's-eye-view (BEV) is trending as cameras are cost-effective for mass production in autonomous driving industry. However, there exists a distinct performance gap between multi-camera BEV and LiDAR based 3D object detection. On...
Implementation of SimMOD: A Simple Baseline for Multi-Camera 3D Object Detection. (AAAI 2023) Arxiv Installation Checkinstallationfor installation. Data Preparation Checkdata_preparationfor preparing the nuScenes dataset. Getting Started To train SimMOD with 8 GPUs, run: ...
3D object detection from visual information is a long-standing challenge for low-cost autonomous driving systems. While object detection from point clouds collected using modalities like LiDAR benefits from information about the 3D structure of visible objects, the camera-based setting is even more ill...