UE网络-零散的积累

动态压缩Vector

根据FVector三个分量的最大值, 压缩Vector, 节省带宽. 这里还要注意一点, 当数值比较大时, 可能压缩后比压缩前占用的bit数量还要大, 即更占用带宽.

// FRepMovement.SerializeQuantizedVector 
bool SerializeQuantizedVector(FArchive& Ar, FVector& Vector, EVectorQuantization QuantizationLevel)
{
    // Since FRepMovement used to use FVector_NetQuantize100, we're allowing enough bits per component
    // regardless of the quantization level so that we can still support at least the same maximum magnitude
    // (2^30 / 100, or ~10 million).
    // This uses no inherent extra bandwidth since we're still using the same number of bits to store the
    // bits-per-component value. Of course, larger magnitudes will still use more bandwidth,
    // as has always been the case.
    // 因为FrepMovement使用过FVector_NetQuantize100, 允许每个分量有足够多的bit来忽略量化登记, 所以仍然支持至少同等量级.
    // 他不会使用额外带宽, 因为我们已经使用同等数量的bit来存储每个分量的bit值. 当然, 更大量级将会使用更多带宽.
    switch(QuantizationLevel)
    {
        case EVectorQuantization::RoundTwoDecimals:
        { // 保留两位小数
            return SerializePackedVector<100, 30>(Vector, Ar);
        }

        case EVectorQuantization::RoundOneDecimal:
        { // 保留一位小数
            return SerializePackedVector<10, 27>(Vector, Ar);
        }

        default:
        { // 默认不保留小数
            return SerializePackedVector<1, 24>(Vector, Ar);
        }
    }
}

详细解析WritePackedVector函数.