Firstly, we introduce a novel sorted matrix decomposition algorithm inspired by the fixed frustum projection and a Hight-compression-based prime extraction method to improve the efficiency of BEV pooing. This approach effectively mitigates the problem of redundant BEV pooling and results in faster ...
是BEV空间特征。使用FTM操作消除了LSS中的voxel pooling的操作,然而,3D空间和BEV网格之间的稀疏映射会导致大量且高度稀疏的FTM,从而损害基于矩阵的VT的效率。为了解决没有定制算子的稀疏映射问题,作者提出了两种技术来减少FTM的稀疏性并加快转换。 2. Prime Extraction和Ring & Ray分解 (1) Prime Extraction 高维的中...
Efficient urban-scale point clouds segmentation with bev projection. ArXiv Preprint abs/2109.09074. Google Scholar Cited by (5) 3D-UMamba: 3D U-Net with state space model for semantic segmentation of multi-source LiDAR point clouds 2025, International Journal of Applied Earth Observation and ...
3.具体方法 - Point-based Attentive ContFuse Module 2d-3d features fusion 的重点问题是:不同的数据结构和percpective,将lidar 原始点云转化为BEV或伪视图可以解决数据结构的问题,但是视角还是不同,并且存在精度丢失的问题,在z轴上会存在neighbor search 和fusion的错误,并且不是逐点的特征。 PACF Module PACF具体...
Additionally, we have implemented the sparse counterpart of pooling lay- ers. In contrast to conventional dense workloads, where the mapping between logical coordinates and physical storage location is straightforward (e.g., on an H × W image, pixel (h, w) is stored at memory location hW +...
论文中的实验设计是通过在KITTI、JRDB和NuScenes三个大型基准测试平台上对PiFeNet和其他状态-of-the-art方法进行比较,同时还进行了详细的权重分析以评估PiFeNet的各个组件的贡献。实验结果表明,PiFeNet在BEV人体检测方面取得了最先进的表现。 以上信息主要来源于第4,6,3页 ...
Specifically, we adopt CUDA-enabled Voxel Pooling [33] implementation and modify it to aggregate features within each BEV grid using average pooling instead of summation. It helps the network to predict a more consistent BEV feature map regardless of the distance to the ego vehicle since a closer...
In the first stage, a traditional convolution module is employed, depicted in Figure 3a, which includes convolution, ReLU activation, and MaxPooling operations. This module expands the spatial features and performs 2× downsampling on the input image. The second to fourth stages use our custom PI...
MAPE first encodes points, then utilizes max-pooling encoding to aggregate features within pillars, then employs attention-pooling encoding to capture fine-grained features, and finally merges the two. This method effectively improves recognition capability for small objects. Ref. [21] introduces a ...
MAPE first encodes points, then utilizes max-pooling encoding to aggregate features within pillars, then employs attention-pooling encoding to capture fine-grained features, and finally merges the two. This method effectively improves recognition capability for small objects. Ref. [21] introduces a ...