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c3ec853 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 | """
PointNet implementation for point cloud feature extraction.
This module implements the PointNet architecture for encoding point clouds into
global or per-point features. It can be used as a conditional encoder in cVAE models.
Reference:
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R. Qi et al., CVPR 2017
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Union
class SpatialTransformer3D(nn.Module):
"""
Spatial Transformer Network for 3D point clouds.
Predicts a 3x3 transformation matrix to canonicalize input point clouds.
"""
def __init__(self):
super(SpatialTransformer3D, self).__init__()
self.conv1 = nn.Conv1d(3, 64, 1)
self.conv2 = nn.Conv1d(64, 128, 1)
self.conv3 = nn.Conv1d(128, 1024, 1)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 9)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.bn4 = nn.BatchNorm1d(512)
self.bn5 = nn.BatchNorm1d(256)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass of the spatial transformer.
Args:
x: Input point cloud of shape (B, 3, N)
Returns:
Transformation matrix of shape (B, 3, 3)
"""
batch_size = x.size(0)
device = x.device
# Encode point cloud
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
# Global max pooling
x = torch.max(x, 2, keepdim=True)[0]
x = x.view(batch_size, -1)
# Predict transformation
x = F.relu(self.bn4(self.fc1(x)))
x = F.relu(self.bn5(self.fc2(x)))
x = self.fc3(x)
# Add identity matrix as residual
identity = torch.eye(3, dtype=x.dtype, device=device).view(1, 9)
identity = identity.repeat(batch_size, 1)
x = x + identity
x = x.view(batch_size, 3, 3)
return x
class SpatialTransformerKD(nn.Module):
"""
Spatial Transformer Network for K-dimensional features.
Predicts a KxK transformation matrix for feature space alignment.
Args:
input_dim: Dimensionality of input features
feature_dim: Dimensionality of intermediate features (default: 1024)
"""
def __init__(self, input_dim: int = 64, feature_dim: int = 1024):
super(SpatialTransformerKD, self).__init__()
self.input_dim = input_dim
self.feature_dim = feature_dim
self.conv1 = nn.Conv1d(input_dim, 64, 1)
self.conv2 = nn.Conv1d(64, 128, 1)
self.conv3 = nn.Conv1d(128, feature_dim, 1)
self.fc1 = nn.Linear(feature_dim, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, input_dim * input_dim)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(feature_dim)
self.bn4 = nn.BatchNorm1d(512)
self.bn5 = nn.BatchNorm1d(256)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass of the spatial transformer.
Args:
x: Input features of shape (B, K, N)
Returns:
Transformation matrix of shape (B, K, K)
"""
batch_size = x.size(0)
device = x.device
# Encode features
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
# Global max pooling
x = torch.max(x, 2, keepdim=True)[0]
x = x.view(batch_size, -1)
# Predict transformation
x = F.relu(self.bn4(self.fc1(x)))
x = F.relu(self.bn5(self.fc2(x)))
x = self.fc3(x)
# Add identity matrix as residual
identity = torch.eye(self.input_dim, dtype=x.dtype, device=device)
identity = identity.view(1, self.input_dim * self.input_dim)
identity = identity.repeat(batch_size, 1)
x = x + identity
x = x.view(batch_size, self.input_dim, self.input_dim)
return x
class PointNetEncoder(nn.Module):
"""
PointNet encoder for extracting features from point clouds.
This encoder can output either global features (for the entire point cloud)
or per-point features (combining global and local information).
Args:
input_dim: Dimensionality of input point features (default: 3 for XYZ)
feature_dim: Dimensionality of output global features (default: 256)
use_spatial_transformer: Whether to use spatial transformer network (default: False)
return_global_feature: If True, return global feature; if False, return per-point features (default: True)
Input:
Point cloud of shape (B, input_dim, N) where:
B = batch size
input_dim = feature dimension per point (e.g., 3 for XYZ)
N = number of points
Output:
If return_global_feature=True:
Global feature of shape (B, feature_dim)
If return_global_feature=False:
Per-point features of shape (B, N, feature_dim + 64)
"""
def __init__(
self,
input_dim: int = 3,
feature_dim: int = 256,
use_spatial_transformer: bool = False,
return_global_feature: bool = True
):
super(PointNetEncoder, self).__init__()
self.input_dim = input_dim
self.feature_dim = feature_dim
self.use_spatial_transformer = use_spatial_transformer
self.return_global_feature = return_global_feature
# Spatial transformer
if use_spatial_transformer:
self.stn = SpatialTransformerKD(input_dim=input_dim, feature_dim=feature_dim)
# Feature extraction layers
self.conv1 = nn.Conv1d(input_dim, 64, 1)
self.conv2 = nn.Conv1d(64, 128, 1)
self.conv3 = nn.Conv1d(128, feature_dim, 1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(feature_dim)
def forward(
self,
x: torch.Tensor,
return_transform: bool = False
) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
"""
Forward pass of PointNet encoder.
Args:
x: Input point cloud of shape (B, input_dim, N)
return_transform: If True, also return the transformation matrix (default: False)
Returns:
If return_transform=False:
features: Encoded features
If return_transform=True:
(features, transform): Tuple of features and transformation matrix
"""
num_points = x.size(2)
transform = None
# Apply spatial transformation
if self.use_spatial_transformer:
transform = self.stn(x)
x = x.transpose(2, 1) # (B, N, K)
x = torch.bmm(x, transform) # (B, N, K)
x = x.transpose(2, 1) # (B, K, N)
# Extract features
x = F.relu(self.bn1(self.conv1(x)))
local_features = x # Save for per-point features
x = F.relu(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
# Global max pooling
global_features = torch.max(x, 2, keepdim=True)[0]
global_features = global_features.view(-1, self.feature_dim)
# Return based on mode
if self.return_global_feature:
features = global_features
else:
# Concatenate global and local features for per-point features
global_features_expanded = global_features.view(-1, self.feature_dim, 1)
global_features_expanded = global_features_expanded.repeat(1, 1, num_points)
features = torch.cat([global_features_expanded, local_features], dim=1)
# Transpose to (B, N, feature_dim + 64)
features = features.transpose(1, 2)
if return_transform:
return features, transform
return features
def get_output_dim(self) -> int:
"""
Get the output feature dimensionality.
Returns:
Output dimension based on the configuration
"""
if self.return_global_feature:
return self.feature_dim
else:
return self.feature_dim + 64
def create_pointnet_encoder(
input_dim: int = 3,
feature_dim: int = 256,
use_spatial_transformer: bool = False,
return_global_feature: bool = True
) -> PointNetEncoder:
"""
Factory function to create a PointNet encoder with specified configuration.
Args:
input_dim: Dimensionality of input point features
feature_dim: Dimensionality of output global features
use_spatial_transformer: Whether to use spatial transformer network
return_global_feature: Whether to return global or per-point features
Returns:
Configured PointNetEncoder instance
"""
return PointNetEncoder(
input_dim=input_dim,
feature_dim=feature_dim,
use_spatial_transformer=use_spatial_transformer,
return_global_feature=return_global_feature
)
if __name__ == '__main__':
print("Testing PointNetEncoder with different configurations...\n")
# Test data
batch_size = 32
num_points = 6890 # SMPL mesh vertices
input_dim = 3
sim_data = torch.randn(batch_size, input_dim, num_points)
configs = [
(True, False, "Global features without STN"),
(True, True, "Global features with STN"),
(False, False, "Per-point features without STN"),
(False, True, "Per-point features with STN"),
]
for return_global, use_stn, desc in configs:
encoder = create_pointnet_encoder(
input_dim=input_dim,
feature_dim=256,
use_spatial_transformer=use_stn,
return_global_feature=return_global
)
output = encoder(sim_data)
print(f"{desc}:")
print(f" Input shape: {sim_data.shape}")
print(f" Output shape: {output.shape}")
print(f" Output dim: {encoder.get_output_dim()}")
print()
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