blocks|key|4797991|text|正如你所说的，你是这个领域的新手，我将努力保持这个高水平。如果有什么不清楚的地方，请发表意见。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4797992|您所指的管道有三个关键阶段：|4797993|4797994|Integration|unordered-list-item|4797995|Rendering|4797996|Pose+Estimation|4797997|4797998|集成阶段从当前姿势下的深度图(Kinect图像)中获取unprojected+points，并将它们“集成”到空间数据结构(体素体积，如Signed+Distance+Function或分层结构，如Octree)中，通常通过维护每个体素的运行平均值。|offset|length|4797999|渲染阶段采用当前帧的反向姿势，并生成当前视图中模型的可见部分的图像。对于常用的体积表示，这是由Raycasting实现的。此阶段的输出提供了下一个活动帧注册到的模型的点(下一个阶段)。|4798000|姿态估计阶段将先前提取的模型点注册到实况帧的那些点。这通常是通过Iterative+Closest+Point算法实现的。|4798001|关于相关文献，我建议以以下论文为起点。|4798002|KinectFusion:+Real-Time+Dense+Surface+Mapping+and+Tracking+|4798003|Real-time+3D+Reconstruction+at+Scale+using+Voxel+Hashing|4798004|Very+High+Frame+Rate+Volumetric+Integration+of+Depth+Images+on+Mobile+Devices|4798005|entityMap|0|LINK|mutability|MUTABLE|url|https://en.wikipedia.org/wiki/Camera_matrix|1|https://en.wikipedia.org/wiki/Signed_distance_function|2|https://en.wikipedia.org/wiki/Octree|3|https://en.wikipedia.org/wiki/Ray_casting|4|https://en.wikipedia.org/wiki/Iterative_closest_point|5|https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ismar2011.pdf|6|https://graphics.stanford.edu/~niessner/papers/2013/4hashing/niessner2013hashing.pdf|7|http://www.robots.ox.ac.uk/%257Evictor/infinitam/files/ismar15infinitam.pdf^0|0|0|0|0|0|0|0|R|I|0|1W|O|1|2R|6|2|0|1B|A|3|0|W|N|4|0|0|0|1N|5|0|0|1K|6|0|0|25|7|0^^$0|@$1|2|3|4|5|6|7|1O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1P|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|1Q|8|@]|9|@]|A|$]]|$1|E|3|F|5|G|7|1R|8|@]|9|@]|A|$]]|$1|H|3|I|5|G|7|1S|8|@]|9|@]|A|$]]|$1|J|3|K|5|G|7|1T|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|1U|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|1V|8|@]|9|@$O|1W|P|1X|1|1Y]|$O|1Z|P|20|1|21]|$O|22|P|23|1|24]]|A|$]]|$1|Q|3|R|5|6|7|25|8|@]|9|@$O|26|P|27|1|28]]|A|$]]|$1|S|3|T|5|6|7|29|8|@]|9|@$O|2A|P|2B|1|2C]]|A|$]]|$1|U|3|V|5|6|7|2D|8|@]|9|@]|A|$]]|$1|W|3|X|5|6|7|2E|8|@]|9|@$O|2F|P|2G|1|2H]]|A|$]]|$1|Y|3|Z|5|6|7|2I|8|@]|9|@$O|2J|P|2K|1|2L]]|A|$]]|$1|10|3|11|5|6|7|2M|8|@]|9|@$O|2N|P|2O|1|2P]]|A|$]]|$1|12|3|-4|5|6|7|2Q|8|@]|9|@]|A|$]]]|13|$14|$5|15|16|17|A|$18|19]]|1A|$5|15|16|17|A|$18|1B]]|1C|$5|15|16|17|A|$18|1D]]|1E|$5|15|16|17|A|$18|1F]]|1G|$5|15|16|17|A|$18|1H]]|1I|$5|15|16|17|A|$18|1J]]|1K|$5|15|16|17|A|$18|1L]]|1M|$5|15|16|17|A|$18|1N]]]]

As you have stated that you are new to this field, I shall attempt to keep this high level. Please do comment if there is something that is not clear.

The pipeline you refer to has three key stages:

<ul>
<li>Integration</li>
<li>Rendering</li>
<li>Pose Estimation</li>
</ul>

The Integration stage takes the <a href="https://en.wikipedia.org/wiki/Camera_matrix" rel="nofollow noreferrer">unprojected points</a> from a Depth Map (Kinect image) under the current pose and "integrates" them into a spatial data structure (a Voxel Volume such as a <a href="https://en.wikipedia.org/wiki/Signed_distance_function" rel="nofollow noreferrer">Signed Distance Function</a> or a hierarchical structure like an <a href="https://en.wikipedia.org/wiki/Octree" rel="nofollow noreferrer">Octree</a>), often by maintaining per Voxel running averages.

The Rendering stage takes the inverse pose for the current frame and produces an image of the visible parts of the model currently in view. For the common volumetric representations this is achieved by <a href="https://en.wikipedia.org/wiki/Ray_casting" rel="nofollow noreferrer">Raycasting</a>. The output of this stage provides the points of the model to which the next live frame is registered (the next stage).

The Pose Estimation stage registers the previously extracted model points to those of the live frame. This is commonly achieved by the <a href="https://en.wikipedia.org/wiki/Iterative_closest_point" rel="nofollow noreferrer">Iterative Closest Point</a> algorithm.

With regards to pertinent literature, I would advise the following papers as a starting point.

<a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ismar2011.pdf" rel="nofollow noreferrer">KinectFusion: Real-Time Dense Surface Mapping and Tracking
</a>

<a href="https://graphics.stanford.edu/~niessner/papers/2013/4hashing/niessner2013hashing.pdf" rel="nofollow noreferrer">Real-time 3D Reconstruction at Scale using Voxel Hashing</a>

<a href="http://www.robots.ox.ac.uk/%7Evictor/infinitam/files/ismar15infinitam.pdf" rel="nofollow noreferrer">Very High Frame Rate Volumetric Integration of Depth Images on
Mobile Devices</a>

I am facing a problem on 3D reconstruction since I am a new to this filed. I have some different views' depth map(point clouds), I want to use them to reconstruct the scene to get the effect like using the kinect fusion. Is there any paper of source code to settle this problem. Or any ideas on this problem.
PS:the point cloud is stored as a file with (x,y,z), you can check <a href="https://pan.baidu.com/s/1miNVPnq" rel="nofollow noreferrer">here</a> to get the data.
Thank you very much.

how to reconstruct scene from different views' point clouds

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我面临着3D重建的问题，因为我是这个领域的新手。我有一些不同视图的深度图(点云)，我想用它们来重建场景，以获得像使用kinect融合一样的效果。是否有任何文件的源代码来解决这个问题。或者任何关于这个问题的想法。PS:点云存储为(x，y，z)文件，您可以检查以获取数据。非常感谢。

问如何从不同视角的点云重建场景
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