SHOT描述子二值化代码

以下是使用PyTorch实现SHOT描述子二值化的示例代码：

import torch
import numpy as np

def binary_encode(x, threshold=0):
    """
    将输入的向量进行二值化编码
    :param x: 输入的向量，可以是numpy数组或PyTorch张量
    :param threshold: 阈值，用于将向量中小于等于该值的元素设置为0，大于该值的元素设置为1，默认为0
    :return: 二值化后的向量
    """
    if isinstance(x, np.ndarray):
        x = torch.from_numpy(x)
    x_binary = torch.zeros_like(x)
    x_binary[x > threshold] = 1
    x_binary[x <= threshold] = 0
    return x_binary

def shot_descriptor(points, normals, pcd, search_radius, num_angles=9, num_bins=10):
    """
    计算SHOT描述子
    :param points: 关键点的坐标，大小为[N,3]
    :param normals: 关键点的法向量，大小为[N,3]
    :param pcd: 点云数据，大小为[M,3]
    :param search_radius: 搜索半径
    :param num_angles: 角度分割数，默认为9
    :param num_bins: 距离分割数，默认为10
    :return: SHOT描述子，大小为[N,num_angles*num_bins*2]
    """
    N = points.shape[0]
    M = pcd.shape[0]
    device = points.device

    # 计算每个关键点的KNN
    knn_indices = torch.empty((N, 100), dtype=torch.long, device=device)
    knn_dists = torch.empty((N, 100), dtype=torch.float32, device=device)
    pcd_tensor = torch.from_numpy(pcd).to(device)
    for i in range(N):
        query_point = points[i].unsqueeze(0)
        dists, indices = torch.topk(torch.norm(pcd_tensor - query_point, dim=1), k=100, largest=False)
        knn_indices[i] = indices
        knn_dists[i] = dists

    # 计算每个关键点的SHOT描述子
    descriptors = torch.zeros((N, num_angles * num_bins * 2), dtype=torch.float32, device=device)
    for i in range(N):
        query_point = points[i]
        query_normal = normals[i]
        knn = pcd_tensor[knn_indices[i]]
        knn_normals = normals[knn_indices[i]]

        # 对每个KNN点计算相对坐标和距离
        knn_coords = knn - query_point.unsqueeze(0)
        knn_dists_norm = knn_dists[i] / search_radius

        # 将相对坐标和距离组合成新的特征向量
        knn_features = torch.cat((knn_coords, knn_dists_norm.unsqueeze(1)), dim=1)

        # 计算每个特征向量的角度和距离
        angles = torch.acos(torch.sum(knn_features * query_normal.unsqueeze(0), dim=1) / torch.norm(knn_features, dim=1))
        distances = torch.norm(knn_features[:, :3], dim=1)

        # 将角度和距离进行分组
        angle_bins = torch.floor(angles / (np.pi / num_angles)).type(torch.long)
        dist_bins = torch.floor(distances / (search_radius / num_bins)).type(torch.long)

        # 对每个分组进行编码
        for j in range(num_angles):
            for k in range(num_bins):
                indices = (angle_bins == j) & (dist_bins == k)
                if indices.any():
                    # 对每个分组内的特征向量进行平均
                    features = knn_features[indices]
                    mean_features = torch.mean(features, dim=0)

                    # 将每个分组的平均向量作为描述子的一部分
                    start_idx = j * num_bins * 2 + k * 2
                    descriptors[i, start_idx:start_idx+2] = mean_features[:2].cpu()

    # 对每个描述子进行二值化编码
    binary_descriptors = binary_encode(descriptors)

    return binary_descriptors

这段代码实现了计算SHOT描述子，并对每个描述子进行二值化编码。其中，`points`和`normals`分别是关键点的坐标和法向量，`pcd`是点云数据，`search_radius`是搜索半径，`num_angles`和`num_bins`分别是角度和距离的分割数。函数返回二值化后的SHOT描述子。