STL:在大量数据中进行分类和搜索

我有两组点(source和target(。目的是为每个source点找到唯一1：1最近的邻居 target中的点。

我的尝试是按预期工作，但慢慢慢。我已经测试了几千分，但是在实际情况下，将是数百万。我不是 stl 的专家。有什么建议我如何优化它？

std::vector<UT_Vector3> targetPosVector;
for (auto i = 0; i < targetNumPoints; i++)
{
    auto pos = target->getPos3(i);
    targetPosVector.push_back(pos);
}
std::vector<int> uniqueNeighborVector;
for (auto ptoff = 0; ptoff < sourceNumPoints; ptoff++)
{
    std::vector<std::pair<int, fpreal>> nearpointVector; // neighbor vector in form of "(idx, dist)"
    auto pos = source->getPos3(ptoff);
    for (auto j = 0; j < targetNumPoints; j++)
    {
        fpreal dist = pos.distance(targetPosVector[j]);
        std::pair<int, fpreal> neighbor {j, dist};
        nearpointVector.push_back(neighbor);
    }
    std::sort(nearpointVector.begin(), nearpointVector.end(), [](const std::pair<int, fpreal> &left,
                                                                 const std::pair<int, fpreal> &right)
                                                                { return left.second < right.second; });
    std::vector<int> neighborVector;
    for (auto i : nearpointVector)
    {
        neighborVector.push_back(i.first);
    }
    // trying to imitate Python's next() function
    // uniqueNeighborList[]
    // uneighbor = next(i for i in neighborVector if i not in uniqueNeighborVector)
    // uniqueNeighborVector = set(uniqueNeighborList.append(uneighbor))
    for (auto i : neighborVector)
    {     
        if (std::find(uniqueNeighborVector.begin(), uniqueNeighborVector.end(), i) == uniqueNeighborVector.end())
        {
            int uneighbor = i; // later on binds to the geometry attribute
            uniqueNeighborVector.push_back(i);
            break;
        }
    }
}

其中：

• source和target是详细信息几何数据
• distance是计算两个向量之间距离的函数
• getPos3是获得3-float vector点位置的函数
• fpreal aka 64-bit float
• UT_Vector3是3-float vector
• sourceNumPoints和 targetNumPoints是 source和target几何形状。