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| # 示例2：基于体素网格的快速神经渲染
import torch
import torch.nn.functional as F
class VoxelRenderer(nn.Module):
"""基于体素网格的快速神经渲染器"""
def __init__(self, voxel_size=32):
super().__init__()
# 可学习的体素网格
self.voxels = nn.Parameter(torch.randn(1, 4, voxel_size, voxel_size, voxel_size))
# 4个通道：RGB(3) + 密度(1)
def forward(self, ray_dirs):
"""
沿光线方向采样体素网格
ray_dirs: 光线方向 (batch, 3)
"""
batch_size = ray_dirs.shape[0]
# 简化的光线追踪（实际应用需要更复杂的采样）
x = torch.linspace(-1, 1, self.voxels.shape[2])
y = torch.linspace(-1, 1, self.voxels.shape[3])
z = torch.linspace(-1, 1, self.voxels.shape[4])
# 生成采样点
samples = torch.stack(torch.meshgrid(x, y, z), dim=-1) # (32,32,32,3)
samples = samples.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) # (batch,32,32,32,3)
# 三次线性插值采样体素网格
sampled_features = F.grid_sample(
self.voxels.repeat(batch_size, 1, 1, 1, 1),
samples,
mode='bilinear',
align_corners=False
)
# 返回沿光线方向累积的颜色和密度
return sampled_features.mean(dim=[2,3,4]) # 简化的体积渲染
# 测试渲染器
def test_voxel_renderer():
batch_size = 4
ray_dirs = torch.randn(batch_size, 3) # 随机光线方向
renderer = VoxelRenderer(voxel_size=32)
output = renderer(ray_dirs)
print(f"输入光线方向形状: {ray_dirs.shape}")
print(f"输出渲染结果形状: {output.shape}") # 应为(4,4)，RGB+密度
test_voxel_renderer()
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