### PointNet++在PyTorch中的实现代码
PointNet++ 是一种基于点云数据的深度学习架构,用于三维分类和分割任务。其 PyTorch 实现可以在多个开源项目中找到,以下是一个具体的实现示例[^1]。
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class PointNetSetAbstraction(nn.Module):
def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all):
super(PointNetSetAbstraction, self).__init__()
self.npoint = npoint
self.radius = radius
self.nsample = nsample
self.mlp_convs = nn.ModuleList()
self.mlp_bns = nn.ModuleList()
last_channel = in_channel
for out_channel in mlp:
self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))
self.mlp_bns.append(nn.BatchNorm2d(out_channel))
last_channel = out_channel
self.group_all = group_all
def forward(self, xyz, points):
"""
Input:
xyz: input points position data, [B, C, N]
points: input points data, [B, D, N]
Return:
new_xyz: sampled points position data, [B, C, S]
new_points_concat: sample points feature data, [B, D', S]
"""
# 采样与分组逻辑(省略具体实现)
new_xyz = index_points(xyz, farthest_point_sample(xyz, self.npoint))
grouped_xyz, grouped_points = query_and_group(self.npoint, self.radius, self.nsample, xyz, new_xyz, points)
# MLP 处理
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
grouped_points = F.relu(bn(conv(grouped_points)))
new_points = torch.max(grouped_points, 2)[0]
return new_xyz, new_points
class PointNetFeaturePropagation(nn.Module):
def __init__(self, in_channel, mlp):
super(PointNetFeaturePropagation, self).__init__()
self.mlp_convs = nn.ModuleList()
self.mlp_bns = nn.ModuleList()
last_channel = in_channel
for out_channel in mlp:
self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))
self.mlp_bns.append(nn.BatchNorm1d(out_channel))
last_channel = out_channel
def forward(self, xyz1, xyz2, points1, points2):
"""
Input:
xyz1: input points position data, [B, C, N]
xyz2: sampled input points position data, [B, C, S]
points1: input points data, [B, D, N]
points2: input points data, [B, D, S]
Return:
new_points: upsampled points data, [B, D', N]
"""
dist, idx = three_nn(xyz1.permute(0, 2, 1), xyz2.permute(0, 2, 1))
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight = dist_recip / norm
interpolated_points = three_interpolate(points2, idx, weight)
if points1 is not None:
new_points = torch.cat([points1, interpolated_points], dim=1)
else:
new_points = interpolated_points
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
new_points = F.relu(bn(conv(new_points)))
return new_points
```
上述代码片段展示了 PointNet++ 的两个核心模块:`PointNetSetAbstraction` 和 `PointNetFeaturePropagation`。这些模块分别实现了点云的分层特征提取和特征传播功能[^2]。
此外,完整的 PointNet++ 实现代码可以在以下 GitHub 仓库中找到:
- **PointNet2.PyTorch**: [https://gitcode.com/gh_mirrors/po/Pointnet2.PyTorch](https://gitcode.com/gh_mirrors/po/Pointnet2.PyTorch) [^1]
- **Pointnet_Pointnet2_pytorch**: [https://gitcode.com/gh_mirrors/po/Pointnet_Pointnet2_pytorch](https://gitcode.com/gh_mirrors/po/Pointnet_Pointnet2_pytorch) [^2]
这些仓库提供了完整的训练脚本、模型定义以及预训练权重,适合开发者快速上手并进行研究或应用开发。
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