前两个阶段只进行粗略的校准,通过联合学习校准任务、分类任务、bbox回归任务,可以在不增加时间成本的情况下完成对粗略校准的具有鲁棒性准确的预测;校准方法可以利用图像的翻转来实现; 在multi-oriented FDDB和a subset of WIDER FACE dataset上以非常快的速度到达了优秀的性能; Progressive Calibration Networks PCN检测流...
训练数据生成:将常用的upright人脸数据集做[-180, 180]旋转,以扩充为旋转数据集 --- Based on the upright faces dataset, werotate the training images with different RIP angles,forming a new training set containing faces with 360 RIP angles; 样本类型和用于不同任务的数据分布: 正样本:iou vs gt > ...
To train the models on pcn dataset: python train_edge.py --train_pcn; --loss_type: pcn; --train_path: the training data; --eval_path: the validation data; --n_gt_points: 16384; --n_out_points: 16384; --density_weight:1e11; ...
We also conduct visualization experiment on fine-grained Stanford Dogs dataset and verify our motivation. Additionally, we apply D-PCN for segmentation on PASCAL VOC 2012 and also find promotion.doi:10.1007/978-3-030-20887-5_38Yang, Shiqi...
PCN is a learning-based shape completion method which directly maps a partial point cloud to a dense, complete point cloud without any voxelization. It is based on our 3DV 2018 publication PCN: Point Completion Network. Please refer to our project website or read our paper for more details....
We use Shapenet dataset [49] derived from Yuan et al.’s data [1]. The training data have partial shapes with the fixed number of points (N=1024). We randomly sample M points from the ground truth of Yuan et al.’s data as our ground truth. The split of training and testing is ...
Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset. 展开 关键词: shape completion learning on point clouds 3D ...
Our network achieves comparable performance on the NTU RGB+D 60 dataset, the NTU RGB+D 120 dataset and the Northwestern-UCLA dataset while considering both accuracy and calculation parameters.doi:10.3233/aic-220268Qinyang ZengRonghao DangQin Fang...
If a monitoring dataset is large, then averaging over the whole set may give relatively consistent results. However, in this case, we lose all the dependencies of monitored parameters on different external factors (e.g., wind direction and speed, fluctuations of the source, etc.) varying ...
The design and development of a bespoke internet-based database was fundamental to achieving this aim.#Following consultation with UKPCN members and agreement on a minimal dataset, the Preterm Clinical Network (PCN) Database was constructed to collect data from women at risk of preterm birth and...