下图显示了应用不同采样方法下的效果,其中DP 代表random input dropout,SSG 代表single grouping : 3.4 点集分割中的特征传播 这主要使用在segmentation部分,主要思想是将特征从下采样的点传播给原始点,如下图所示。 这里主要有两个方法:inverse distance weighted average:反距离权重平均插值。这里用kNN,也就是邻近的...
ssgsingle-scale-grouping单尺度分组(详见论文内介绍) mlpmulti-layer-perceptron多层感知器(简单理解就是for循环添加一堆大小不一样的Dense层的意思) tqdm一个python进度条展示库 bnbatch normalization批次正则化,有什么作用可自行google accaccuracy准确率
pointnet2_ssg python train_semseg.py --model pointnet2_sem_seg --test_area 5 --log_dir pointnet2_sem_seg python test_semseg.py --log_dir pointnet2_sem_seg --test_area 5 --visual Visualization results will save in log/sem_seg/pointnet2_sem_seg/visual/ and you can visualize these ....
3:,:]xyz=xyz[:,:3,:]else:norm=None# l1_points作为sa1的特征输出l1_xyz,l1_points=self.sa1(xyz,norm)# l2_points作为sa2的特征输出l2_xyz,l2_points=self.sa2(l1_xyz,l1_points)# l3_points作为sa3的特征输出l3_xyz,l3_points=self.sa3(l2_xyz,l2_points)...
import tensorflow from pnet2_layers.layers import Pointnet_SA class CLS_SSG_Model(tf.keras.Model) def __init__(self, batch_size, activation=tf.nn.relu): super(Pointnet2Encoder, self).__init__() self.batch_size = batch_size self.layer1 = Pointnet_SA(npoint=512, radius=0.2, nsample...
import torch import torch.nn as nn import torch.nn.functional as F from pointnet2_ops.pointnet2_modules import PointnetFPModule, PointnetSAModule, PointnetSAModuleMSG from utils import weights_init_kaiming, weights_init_classifier import numpy as np class PointNet2SSG(nn.Module): def __init_...
import(name) File "/home/baxter/pointnet2/models/pointnet2_cls_ssg.py", line 13, in from pointnet_util import pointnet_sa_module File "/home/baxter/pointnet2/utils/pointnet_util.py", line 15, in from tf_sampling import farthest_point_sample, gather_point ...
PointNet2_ssg (Pytorch)53.2 Performance on raw dataset still on testing... Visualization Using show3d_balls.py ## build C++ code for visualization cd visualizer bash build.sh ## run one example python show3d_balls.py Using MeshLab Reference By ...
PointNet2_ssg (Pytorch)53.2 Performance on raw dataset still on testing... Visualization Using show3d_balls.py ## build C++ code for visualization cd visualizer bash build.sh ## run one example python show3d_balls.py Using MeshLab Reference By ...
ssg --use_normals --log_dir pointnet2_cls_ssg_normal python test_classification.py --use_normals --log_dir pointnet2_cls_ssg_normal ## e.g., pointnet2_ssg with uniform sampling python train_classification.py --model pointnet2_cls_ssg --use_uniform_sample --log_dir pointnet2_cls_ssg...