点云补全算法汇总-Toy模板网

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点云补全概念

点云补全就是希望基于观察到的残缺不全的点云生成完整的 3D 点云。由于扫描或者距离的原因导致点云局部缺失，对其进行补全，传统算法可能会补不完整，也可能会补的过于完整。

点云补全（PF-Net）

今天这里要讲的是PF-Net: Point Fractal Network for 3D Point Cloud Completion，整体网络模型：
点云补全算法汇总

点云补全网络模型

骨骼点逐级恢复点云

最远点采样

利用骨骼点来逐级恢复点云
在构建标签时依旧选择最远点采样

点云补全算法汇总

特征提取

特征提取，融合多尺度特征，信息更丰富

PF-Net特征提取代码实现如下：

def forward(self,x):
        print(x.shape)
        x = torch.unsqueeze(x,1)
        print(x.shape)
        x = F.relu(self.bn1(self.conv1(x)))
        print(x.shape)
        x = F.relu(self.bn2(self.conv2(x)))
        print(x.shape)
        x_128 = F.relu(self.bn3(self.conv3(x)))
        print(x_128.shape)
        x_256 = F.relu(self.bn4(self.conv4(x_128)))
        x_512 = F.relu(self.bn5(self.conv5(x_256)))
        x_1024 = F.relu(self.bn6(self.conv6(x_512)))
        print(x_1024.shape)
        x_128 = torch.squeeze(self.maxpool(x_128),2)
        print(x_128.shape)
        x_256 = torch.squeeze(self.maxpool(x_256),2)
        x_512 = torch.squeeze(self.maxpool(x_512),2)
        x_1024 = torch.squeeze(self.maxpool(x_1024),2)
        print(x_1024.shape)
        L = [x_1024,x_512,x_256,x_128]
        x = torch.cat(L,1)
        print(x.shape)
        return x

输出各阶段预测点，还考虑骨骼之间的关系

分层预测

PF-Net分层预测代码实现如下：

def forward(self,x):
        print(np.array(x).shape)
        x = self.latentfeature(x)
        print(x.shape)
        x_1 = F.relu(self.fc1(x)) #1024
        print(x_1.shape)
        x_2 = F.relu(self.fc2(x_1)) #512
        print(x_2.shape)
        x_3 = F.relu(self.fc3(x_2))  #256
        print(x_3.shape)
        
        
        pc1_feat = self.fc3_1(x_3)
        print(pc1_feat.shape)
        pc1_xyz = pc1_feat.reshape(-1,64,3) #64x3 center1
        print(pc1_xyz.shape)
        
        pc2_feat = F.relu(self.fc2_1(x_2))
        print(pc2_feat.shape)
        pc2_feat = pc2_feat.reshape(-1,128,64)
        print(pc2_feat.shape)
        pc2_xyz =self.conv2_1(pc2_feat) #6x64 center2
        print(pc2_xyz.shape)
        
        pc3_feat = F.relu(self.fc1_1(x_1))
        print(pc3_feat.shape)
        pc3_feat = pc3_feat.reshape(-1,512,128)
        print(pc3_feat.shape)
        pc3_feat = F.relu(self.conv1_1(pc3_feat))
        print(pc3_feat.shape)
        pc3_feat = F.relu(self.conv1_2(pc3_feat))
        print(pc3_feat.shape)
        pc3_xyz = self.conv1_3(pc3_feat) #12x128 fine
        print(pc3_xyz.shape)
        
        pc1_xyz_expand = torch.unsqueeze(pc1_xyz,2)
        print(pc1_xyz_expand.shape)
        pc2_xyz = pc2_xyz.transpose(1,2)
        print(pc2_xyz.shape)
        pc2_xyz = pc2_xyz.reshape(-1,64,2,3)
        print(pc2_xyz.shape)
        pc2_xyz = pc1_xyz_expand+pc2_xyz
        print(pc2_xyz.shape)
        pc2_xyz = pc2_xyz.reshape(-1,128,3) 
        print(pc2_xyz.shape)
        
        pc2_xyz_expand = torch.unsqueeze(pc2_xyz,2)
        print(pc2_xyz_expand.shape)
        pc3_xyz = pc3_xyz.transpose(1,2)
        print(pc3_xyz.shape)
        pc3_xyz = pc3_xyz.reshape(-1,128,int(self.crop_point_num/128),3)
        print(pc3_xyz.shape)
        pc3_xyz = pc2_xyz_expand+pc3_xyz
        print(pc3_xyz.shape)
        pc3_xyz = pc3_xyz.reshape(-1,self.crop_point_num,3) 
        print(pc3_xyz.shape)
        
        return pc1_xyz,pc2_xyz,pc3_xyz #center1 ,center2 ,fine

Chamfer Distance

Chamfer Distance来衡量预测效果与GT之间的差异

GAN

整体架构还是GAN形式

BCELoss损失模块

BCELoss模块代码实现如下:

import math

r11 = 0 * math.log(0.8707) + (1-0) * math.log((1 - 0.8707))
r12 = 1 * math.log(0.7517) + (1-1) * math.log((1 - 0.7517))
r13 = 1 * math.log(0.8162) + (1-1) * math.log((1 - 0.8162))

r21 = 1 * math.log(0.3411) + (1-1) * math.log((1 - 0.3411))
r22 = 1 * math.log(0.4872) + (1-1) * math.log((1 - 0.4872))
r23 = 1 * math.log(0.6815) + (1-1) * math.log((1 - 0.6815))

r31 = 0 * math.log(0.4847) + (1-0) * math.log((1 - 0.4847))
r32 = 0 * math.log(0.6589) + (1-0) * math.log((1 - 0.6589))
r33 = 0 * math.log(0.5273) + (1-0) * math.log((1 - 0.5273))

r1 = -(r11 + r12 + r13) / 3
#0.8447112733378236
r2 = -(r21 + r22 + r23) / 3
#0.7260397266631787
r3 = -(r31 + r32 + r33) / 3
#0.8292933181294807
bceloss = (r1 + r2 + r3) / 3 
print(bceloss)

判别模块代码实现如下：

 def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x_64 = F.relu(self.bn2(self.conv2(x)))
        x_128 = F.relu(self.bn3(self.conv3(x_64)))
        x_256 = F.relu(self.bn4(self.conv4(x_128)))
        x_64 = torch.squeeze(self.maxpool(x_64))
        x_128 = torch.squeeze(self.maxpool(x_128))
        x_256 = torch.squeeze(self.maxpool(x_256))
        Layers = [x_256,x_128,x_64]
        x = torch.cat(Layers,1)
        x = F.relu(self.bn_1(self.fc1(x)))
        x = F.relu(self.bn_2(self.fc2(x)))
        x = F.relu(self.bn_3(self.fc3(x)))
        x = self.fc4(x)
        return x