计算3D捕获速度

Zivid相机捕获和处理点云所需的时间由以下几个因素决定：

• 视觉引擎

• 采集次数

• 每次采集的曝光时间和光圈

• 过滤器

• 所连接硬件的处理能力

该计算器可让您估算 Zivid 相机的 3D 图像采集时间。这是相机和场景必须相对于彼此保持静止的时间量。 3D图像采集时间是采集API返回所需的时间。

此外，我们还对不同硬件上的许多不同设置进行了基准测试。这些结果包括以上所有内容。

有关点云捕获过程的更多信息，请参阅 点云捕获过程 。

Select camera model:

Select a camera Zivid 2+ LR110 Zivid 2+ MR60 Zivid 2+ MR130 Zivid 2+ L110 Zivid 2+ M60 Zivid 2+ M130 Zivid 2 L100 Zivid 2 M70

Engine:

phase
stripe
omni
sage

Sampling:

all
by2x2
by4x4

Acquisitions:

Acquisition 1
• Exposure Time [us]: 1000
• Aperture: 2.83
Acquisition 2
• Exposure Time [us]: 4000
• Aperture: 2.83
Acquisition 3
• Exposure Time [us]: 20000
• Aperture: 2.83

Add AcquisitionRemove Acquisition

Select preset group:

Manufacturing SettingsParcels SettingsConsumer Goods Settings

Select preset:

Manufacturing Small FeaturesManufacturing SpecularManufacturing DiffuseManufacturing Semi Specular
Reset to preset values

Estimated 3D image acquisition time:

870 ms

Benchmark acquisition time and processing overhead:

Benchmark acquisition time and processing overhead is only available for presets. Acquisition time added with processing gives the total capture time.

	Low-end 1	High-end 2
Acquisition	772.2 ms	768.3 ms
Processing	3915.0 ms	134.7 ms

Estimated max bandwidth:

7.07 Gb/s ³

[1] Intel UHD i5G1
[2] NVIDIA 4070 Ti
[3] The `estimated max bandwidth` provides an estimate of how fast your Ethernet connection should be, at a minimum, if you want to achieve the fastest capture speed.

警告

请注意，在高占空比下使用 Zivid 相机可能会触发热节流以防止过热。有关此主题的更多信息，请查看 投影仪亮度 页面。

相机基准测试

下面您将找到不同类型硬件的不同相机预设的基准。您可以调整相机、配置和显示选项，以找到您最感兴趣的数据。

Cameras

Configurations

Low-end Intel High-end NVIDIA High-end Intel Low-end Jetson NVIDIA High-end Jetson NVIDIA

Details about configurations

Display Options

Standard Deviation

Capture 2D + 3D (hide)

Manufacturing Settings Parcels Settings Consumer Goods Settings Inspection Settings

hidden

hidden

	2+R		2+
	High-end NVIDIA		High-end NVIDIA
	2D	3D	2D	3D
Manufacturing Settings
Manufacturing Diffuse	30 ms (±0)155 ms (±1)180 ms (±1)		50 ms (±3)430 ms (±1)455 ms (±3)
Manufacturing Semi-Specular	30 ms (±0)740 ms (±1)765 ms (±1)		50 ms (±3)690 ms (±1)715 ms (±3)
Manufacturing Specular	47 ms (±0)960 ms (±0)1000 ms (±1)		50 ms (±3)1355 ms (±1)1385 ms (±3)
Manufacturing Small Features	42 ms (±2)890 ms (±1)910 ms (±1)		49 ms (±3)825 ms (±2)850 ms (±4)

备注

请注意，具有相同名称的设置可能因相机型号而异。有关推荐的硬件的更多信息，请参阅 推荐的工业 PC 。

You can check your acquisition and total capture time in Zivid Studio and via the SDK.

C++

source

const auto frame = camera.capture2D3D(settings);
const auto frameInfo = frame.info();
std::cout << "Acquisition time:" << std::endl;
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(
                 frameInfo.metrics().acquisitionTime().value())
                 .count()
          << " ms" << std::endl;
std::cout << "Capture time:" << std::endl;
std::cout
    << std::chrono::duration_cast<std::chrono::milliseconds>(frameInfo.metrics().captureTime().value()).count()
    << " ms" << std::endl;

C#

source

using (var frame = camera.Capture2D3D(settings))
{
    var frameInfo = frame.Info;
    Console.WriteLine("Acquisition time:");
    Console.WriteLine(frameInfo.Metrics.AcquisitionTime.Milliseconds + " ms");
    Console.WriteLine("Capture time:");
    Console.WriteLine(frameInfo.Metrics.CaptureTime.Milliseconds + " ms");

Python

source

frame = camera.capture_2d_3d(settings)
frame_info = frame.info
print("Acquisition time:")
print(f"{frame_info.metrics.acquisition_time.total_seconds() * 1000:.0f} ms")
print("Capture time:")
print(f"{frame_info.metrics.capture_time.total_seconds() * 1000:.0f} ms")

您还可以运行我们的 C++ 示例 ZividBenchmark.cpp 来对您的硬件进行基准测试。

版本历史

SDK	变更
2.12.0	使用低端 GPU 时， 启用颜色的捕获时间减少了至多 10-20%。Zivid 2+ 上的 3D 采集时间减少了约 5%。
2.9.0	捕获（capture）函数将在采集（acquisition ）完成后返回。
2.8.0	提高了Zivid 2的捕获速度。