利用上述point-to-pixel fusion模块生成的特征,进行点云分割;再从前景点生成3D proposal。对于每个3D anchor(尺寸为:L=3.9,W=1.6,H=1.5),将其投影到2D image,以生成2D anchor,以建立2D-3D联系。 对于分类部分:强制2D-3D anchor pairs共享同一个置信度分数。利用上述生成的2D anchor,进行2D 分类预测。2D 分割...
Cross-Modality Knowledge Distillation Network for Monocular 3D Object DetectionLeveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods. However, the existing methods usually apply non-end-to-end training ...
为了解决这一问题,作者将SA和CA集成到一个3D注意张量中:1)通过并行利用增强鲁棒性,降低3D注意方式的计算复杂度;2)在空间维度和通道维度上同时细化单模态特征。 2)考虑到不同模态之间存在很强的相关性和互补性,作者设计了一个cmWR单元来捕捉多个模态之间的长期依赖关系,并从全局角度提炼模态特征。 类似于Non-Local...
CVPR2023 (highlight) - UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View - megvii-research/CVPR2023-UniDistill
Cross-modality fusing complementary information from different modalities effectively improves object detection performance, making it more useful and robust for a wider range of applications. Existing fusion strategies combine different types of images or merge different backbone features through elaborated neu...
Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection (ECCV 2022 Oral) - Cc-Hy/CMKD
Then we concatenate q1, q2, q3 and pass it into a fully connected layer followed by the ℓ2 normalization to get the final sentence embedding c0: c0 = LayerN orm (We concat (q1, q2, q3) + be) , (7) where We ∈ Rd×3d and be ∈ Rd×1. ...
Extensive experiments on the DroneVehicle dataset demonstrate the flexibility and effectiveness of the proposed method for crossmodality vehicle detection. The dataset can be download from this https URL . 展开 关键词: Vehicle detection Object detection Uncertainty Drones Surveillance Lighting Sun ...
A_{3D} = SA(f_{mod}) \otimes CA(f_{mod}) \\ f_{mod}^{smAR} = conv(A_{3D} \odot f_{mod} + f_{mod}) \qquad (5)\\ mod \in \{r, d, rgbd\}\\其中SA计算作者的代码里在最大值池化后有一个核大小为3或7的卷积。 针对第二个问题,作者提出的解决方案是使用cross-modality ...
Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network Convolutional neural networkResidual learningMulti-modality super-resolutionHigh-resolution (HR) magnetic resonance (MR) imaging is an important diagnostic ... JD A,ZH A,LW A,... - 《Neurocom...