3D重建 - 如何从2D图像创建3D模型？ [英] 3D reconstruction -- How to create 3D model from 2D image?
问题描述
如果我用相机拍照，所以我知道从相机到物体的距离，例如房子的比例模型，我想把它变成我可以操纵的3D模型周围所以我可以评论房子的不同部分。
如果我坐下来考虑拍摄多张照片，标记方向和距离，我应该能够弄清楚如何做到这一点，但是，我想我会问是否有人有一些可能有助于解释更多的论文。
你用什么语言解释无所谓，因为我正在寻找最好的方法。
现在我正在考虑展示房子，然后用户可以为高度提供一些帮助，例如作为从相机到模型那部分顶部的距离，并且给予足够的这个可以开始计算其余部分的高度，特别是如果有自上而下的图像，则从四边的角度拍摄图片，计算相对高度。
然后部件需要颜色不同，以帮助分离我期望的模型的各个部分。
研究取得了重大进展，现在可以从2D图像中获得漂亮的3D形状。例如，我们最近的研究工作名为
我们采取的方法有一些与认知科学或大脑工作方式相关的贡献：我们建立的模型为所有形状类别共享参数，而不是仅针对一个类别。此外，它还可以获得一致的表示形式，并在生成3D形状作为输出时将输入视图的不确定性考虑在内。因此，即使对于非常模糊的输入，它也能够自然地给出有意义的结果。如果你看一下我们论文的引用，就可以看到从2D图像到3D形状的更多进展。
If I take a picture with a camera, so I know the distance from the camera to the object, such as a scale model of a house, I would like to turn this into a 3D model that I can maneuver around so I can comment on different parts of the house.
If I sit down and think about taking more than one picture, labeling direction, and distance, I should be able to figure out how to do this, but, I thought I would ask if someone has some paper that may help explain more.
What language you explain in doesn't matter, as I am looking for the best approach.
Right now I am considering showing the house, then the user can put in some assistance for height, such as distance from the camera to the top of that part of the model, and given enough of this it would be possible to start calculating heights for the rest, especially if there is a top-down image, then pictures from angles on the four sides, to calculate relative heights.
Then parts will need to differ in color to help separate out the various parts of the model I expect also.
Research has made significant progress and these days it is possible to obtain pretty good-looking 3D shapes from 2D images. For instance, in our recent research work titled "Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks" took a big step in solving the problem of obtaining 3D shapes from 2D images. In our work, we show that you can not only go from 2D to 3D directly and get a good, approximate 3D reconstruction but you can also learn a distribution of 3D shapes in an efficient manner and generate/synthesize 3D shapes. Below is an image of our work showing that we are able to do 3D reconstruction even from a single silhouette or depth map (on the left). The ground-truth 3D shapes are shown on the right.
The approach we took has some contributions related to cognitive science or the way the brain works: the model we built shares parameters for all shape categories instead of being specific to only one category. Also, it obtains consistent representations and takes the uncertainty of the input view into account when producing a 3D shape as output. Therefore, it is able to naturally give meaningful results even for very ambiguous inputs. If you look at the citation to our paper you can see even more progress just in terms of going from 2D images to 3D shapes.
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