扫描现实世界对象并从中生成3D网格 [英] Scanning Real-World Object and generating 3D Mesh from it

问题描述

ARKit应用程序允许我们创建一个 ARReferenceObject ，并使用它可以可靠地识别实际对象的位置和方向.但是我们也可以保存完成的 .arobject 文件.

ARKit app allows us create an ARReferenceObject, and using it, we can reliably recognize a position and orientation of real-world object. But also we can save the finished .arobject file.

但是， ARReferenceObject 仅包含ARKit识别真实对象所需的空间特征信息，而不是该对象的可显示3D重建.

However, ARReferenceObject contains only the spatial features information needed for ARKit to recognize the real-world object, and is not a displayable 3D reconstruction of that object.

func createReferenceObject(transform: simd_float4x4, 
                              center: simd_float3, 
                              extent: simd_float3, 
                   completionHandler: (ARReferenceObject?, Error?) -> Void)

我的问题:

有没有一种方法可以让我们使用 泊松曲面重构 或 摄影测量 ?

Is there a method that allows us to reconstruct digital 3D geometry (low-poly or high-poly) from .arobject file using Poisson Surface Reconstruction or Photogrammetry?

推荐答案

您用引号

ARReferenceObject 仅包含ARKit识别实际对象所需的空间特征信息，而不是该对象的可显示3D重建.

An ARReferenceObject contains only the spatial feature information needed for ARKit to recognize the real-world object, and is not a displayable 3D reconstruction of that object.

如果您运行示例代码，则可以自己查看其可视化效果在扫描过程中以及经过测试识别后创建参考对象-只是一个稀疏的3D点云.Apple的API为您提供的功能肯定没有摄影测量法，并且在恢复网格中的真实结构方面没有太多事情要做.

If you run that sample code, you can see for yourself the visualizations it creates of the reference object during scanning and after a test recognition — it's just a sparse 3D point cloud. There's certainly no photogrammetry in what Apple's API provides you, and there'd not much to go on in terms of recovering realistic structure in a mesh.

并不是说这样的努力是不可能的-已经有一些第三方演示 此处基于ARKit的摄影测量实验.但是

That's not to say that such efforts are impossible — there have been some third parties demoing Here photogrammetry experiments based on top of ARKit. But

1..它不使用ARKit 2对象扫描，仅使用 ARFrame 中的原始像素缓冲区和特征点.

1. that's not using ARKit 2 object scanning, just the raw pixel buffer and feature points from ARFrame.

2..这些演示中的推断水平需要非平凡的原始R& D，因为它远远超出了信息的种类ARKit本身提供.

2. the level of extrapolation in those demos would require non-trivial original R&D, as it's far beyond the kind of information ARKit itself supplies.

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