No code (you'll see why), but you might be interested to read the 1963 PhD thesis of Lawrence G Roberts, titled Machine perception of three-dimensional solids, where he tackles this very problem. You can view the pdf in the first link.
What you're trying to do is a very, very difficult task. At least if you want this to work with arbitrary shapes.
This is for a simple reason: A computer does not work like a human's brain. Let's for example take a look at that top right picture. What do you see? A Box? Or is it some flat rectangular shape with two parallelograms attached on top and the left?
Our brain sees a 3 dimensional shape, because we live in a 3 dimensional world and the majority of things we see are 3 dimensional and our evolution led to the formation of neural structures which easily lean perception of such.
But there's an even more fundamental problem at hand: Image segmentation. You need to separate the parts of the picture into contigous regions to work on. In our brain object shape recognition, reconstruction and segmentation are interconnected and it happens in a iterative process. You've probably experienced it a number of times: You saw some shape, but at first couldn't tell what it is. And your mind was racing with a huge amount of objects and shapes that may be what you see. And then after a few moments you clearly see the shape. But not because you finally figured it out from just the picture, but because the brain augmented the sensory input with its preexisting knowledge of the world.
The task you're asking for touches not only computer vision, but also machine learning and pattern recognition.
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没有代码（您会明白原因），但您可能有兴趣阅读 1963 年博士论文劳伦斯·G·罗伯茨的a>，题为三维实体的机器感知，他在其中解决了这个问题。您可以在第一个链接中查看 pdf。
No code (you'll see why), but you might be interested to read the 1963 PhD thesis of Lawrence G Roberts, titled Machine perception of three-dimensional solids, where he tackles this very problem. You can view the pdf in the first link.
你要做的事情是一项非常非常困难的任务。至少如果您希望它适用于任意形状。
原因很简单：计算机不像人脑那样工作。例如，让我们看一下右上角的图片。你看到了什么？一个盒子？或者它是一些扁平的矩形形状，顶部和左侧附有两个平行四边形？
我们的大脑看到的是 3 维形状，因为我们生活在 3 维世界中，我们看到的大多数事物都是 3 维的，而我们的进化导致了神经结构的形成，这些神经结构很容易产生这种感知。
但还有一个更根本的问题：图像分割。您需要将图片的各个部分分成连续的区域才能进行处理。在我们的大脑中，物体形状识别、重建和分割是相互关联的，并且发生在迭代过程中。您可能已经经历过很多次：您看到某种形状，但一开始无法分辨它是什么。你的大脑正在思考大量可能是你所看到的物体和形状。过了一会儿，你就清楚地看到了形状。但这并不是因为你最终仅从图片中弄清楚了它，而是因为大脑用其预先存在的世界知识增强了感官输入。
您要求的任务不仅涉及计算机视觉，还涉及机器学习和模式识别。
What you're trying to do is a very, very difficult task. At least if you want this to work with arbitrary shapes.
This is for a simple reason: A computer does not work like a human's brain. Let's for example take a look at that top right picture. What do you see? A Box? Or is it some flat rectangular shape with two parallelograms attached on top and the left?
Our brain sees a 3 dimensional shape, because we live in a 3 dimensional world and the majority of things we see are 3 dimensional and our evolution led to the formation of neural structures which easily lean perception of such.
But there's an even more fundamental problem at hand: Image segmentation. You need to separate the parts of the picture into contigous regions to work on. In our brain object shape recognition, reconstruction and segmentation are interconnected and it happens in a iterative process. You've probably experienced it a number of times: You saw some shape, but at first couldn't tell what it is. And your mind was racing with a huge amount of objects and shapes that may be what you see. And then after a few moments you clearly see the shape. But not because you finally figured it out from just the picture, but because the brain augmented the sensory input with its preexisting knowledge of the world.
The task you're asking for touches not only computer vision, but also machine learning and pattern recognition.
我认为你想做的是 这个。您可以给他们写一封电子邮件以获取源代码。祝你好运！
I think what you want to do is this. You can write them an e-mail to get access to the source code. Good luck!
这是一项非常复杂的任务，涉及多种图像处理技术来实现您正在寻找的结果。
我建议您先看一下这项工作：创建 3D 模型使用简单的网络摄像头
That is a very complex task and involves several image processing techniques to achieve the result you are looking for.
I suggest you take a look at this work first: Creating 3D models with a simple webcam