Incorrect reconstruction result about rgbd-integration-2020 HOT 10 CLOSED

Hi @jly0810!
In my experiment the camera was fixed, and only the person was rotating. However, you can do the opposite: the person can sit fixed, while the camera is moved around him/her.

1. The main requirement is that change between adjacent frames should be small. Do you skip frames with parameter

   RGBD-Integration-2020/main__TSDF_Integrate__color_depth.py

   Line 27 in 42cb668

   skip_N_frames = 10 # ! - Can choose the range of integrated Frames - less frames = faster run

   ? If yes, try decreasing it. In the frames above the change seems too large.
2. The main issue with your frames, as far as I can see, is that you skipped segmentation of the subject step. You should delete the background behind your subject.
   I haven't published the code for segmentation. For every depth frame you should remove all pixels with distance value larger than a threshold. Or, alternatively, for each point cloud you can delete points with z coordinate larger than a threshold.

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Have you tried anything yet?

1. You can try varying distance truncation parameter here:
   

   RGBD-Integration-2020/main__TSDF_Integrate__color_depth.py

   Line 96 in 42cb668

   trunc = np.inf # Maximum depth limit. The Test depth frames were already truncated during Subject segmentation.

2. I looked into the project, two years have passed and I can't remember which of the segmentation scripts I have used 😅
   So I'll upload one that seems to be right.
   It is based on the fact that before capturing the subject, a background .bag is recorded.

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Have you tried anything yet?

1. You can try varying distance truncation parameter here:
   

   RGBD-Integration-2020/main__TSDF_Integrate__color_depth.py

   Line 96 in 42cb668

   trunc = np.inf # Maximum depth limit. The Test depth frames were already truncated during Subject segmentation.

2. I looked into the project, two years have passed and I can't remember which of the segmentation scripts I have used 😅
   So I'll upload one that seems to be right.
   It is based on the fact that before capturing the subject, a background .bag is recorded.

你好@jly0810！ 在我的实验中，相机是固定的，只有人在旋转。但是，您可以做相反的事情：该人可以固定坐着，而相机围绕他/她移动。

1. 主要要求是相邻帧之间的变化应该很小。你用参数跳过帧吗

   RGBD-Integration-2020/main__TSDF_Integrate__color_depth.py

   Line 27 in 42cb668

   skip_N_frames = 10 # ! - Can choose the range of integrated Frames - less frames = faster run

   
   ? 如果是，请尝试减少它。在上面的框架中，变化似乎太大了。
2. 据我所知，您的帧的主要问题是您跳过了主题步骤的分割。您应该删除主题背后的背景。
   我还没有发布分段代码。对于每个深度帧，您应该删除距离值大于阈值的所有像素。或者，对于每个点云，您可以删除 z 坐标大于阈值的点。

Thank you for your reply.

1. My dataset should not meet your requirement that there is little change between adjacent frames, or is there any specific standard? Does this condition mainly affect ICP operation? But，I never skip frames, that is, I always keep “skip_N_frames = 1”
   2.Does your code require background culling? Is this a requirement? My goal is not limited to the reconstruction of portraits, so background removal is not necessary for me. And from my reconstruction results, I don't think whether background removal is the cause of incorrect results. It is more likely to be caused by incorrect external parameters. I'm not sure if my idea is correct, I hope you can correct it!
   I did not operate in this step, but I modified the truncation value, and the reconstruction result is still not correct.

Thanks again for your answer, thank you！

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1. If you look into ICP definition, you can see that it tries to find a rigid transformation (translation+rotation) that would tightly match 2 point clouds in space. So, the closer your adjacent point clouds - the easier it is to find this transform. You can try to tune ICP function parameters to make the alignment converge.
   ICP tries to find matching point pairs in the two point clouds (based on various criteria - the closest point; source point's normal ray intersection with destination surface etc). This means that the clearer are the tracked objects - the better. If you bring along the background wall plane it will surely affect alignment. In the Open3d tutorial example they have a point cloud of a chair with wall background - my guess is: it will work properly only if you move the camera, but don't move the chair relative to the wall. If the chair is moved - it is not clear what objects to match: either align the walls in 2 frames, or align the chairs.

2. However, the first step of the algorithm is rough point clouds alignment with RANSAC:
   http://www.open3d.org/docs/release/tutorial/pipelines/global_registration.html?highlight=ransac
   Only after it, the more delicate ICP is used.
   I would recommend you take 2 of your point clouds and run RANSAC example on them from the link above - and see if they can be aligned.

3. What sensor do you use to record data?

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1. 如果您查看 ICP 定义，您会发现它试图找到与空间中的 2 个点云紧密匹配的刚性变换（平移+旋转）。因此，相邻点云越接近 - 越容易找到这种变换。您可以尝试调整 ICP 函数参数以使对齐收敛。
   ICP 尝试在两个点云中找到匹配的点对（基于各种标准 - 最近的点；源点与目标表面的法线相交等）。这意味着被跟踪的对象越清晰 - 越好。**如果带背景墙平面肯定会影响对齐。**在 Open3d 教程示例中，他们有一个带有墙壁背景的椅子的点云 - 我的猜测是：它只有在您移动相机时才能正常工作，但不要相对于墙壁移动椅子。如果椅子被移动 - 不清楚要匹配哪些对象：要么将墙壁对齐 2 个框架，要么对齐椅子。
2. 不过算法的第一步是粗略的点云与RANSAC对齐：
   http://www.open3d.org/docs/release/tutorial/pipelines/global_registration.html?highlight=ransac
   只有在这之后，ICP才更细腻用过的。
   我建议您从上面的链接中获取 2 个点云并在它们上运行 RANSAC 示例 - 看看它们是否可以对齐。
3. 你用什么传感器来记录数据？

1、In my dataset, only the camera is moved, and objects in the scene do not move relative. The chair and the figure are regarded as one object, so I think there is no problem that you don't know which object to match
2、I'll try this later
3、The data set above is obtained in blender.
In fact, I also got the external parameters of the camera. I tried in the following project, (https://github.com/andyzeng/tsdf-fusion-python)and the reconstruction results did not overlap. I studied for a long time and did not find the problem. I always thought it was the external parameters of the camera, so I found your code to try.In the previous work, I used the external parameters obtained from calibration as input.
I also have the data captured by realsense, which can get correct results in the above project, but the results are incorrect here.

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3. It is strange that your data captured by RealSense fails here (is it RealSense D435?) Have you checked whether the intrinsics of your RealSense camera are same as mine?
   

   RGBD-Integration-2020/main__TSDF_Integrate__color_depth.py

   Lines 103 to 108 in 84f4e4f

   	width = 1280
   	height = 720
   	fx = 920.003
   	fy = 919.888
   	cx = 640.124
   	cy = 358.495

color_stream = profile.get_stream(realsense.stream.color);
color_video_stream = rgb_stream .as('video_stream_profile');
color_intrinsic=depth_aligned_to_color_intrinsic = color_video_stream.get_intrinsic()

In this project it is assumed that you have aligned depth and color frames by means of pyrealsense when recording data (example), so extrinsic parameters are not needed. Only intrinsic parameters of the camera are used to create a point cloud.

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3. 奇怪的是你的RealSense捕获的数据在这里失败了（是RealSense D435吗？）你检查过你的RealSense相机的内在函数是否和我的一样？
   

   RGBD-Integration-2020/main__TSDF_Integrate__color_depth.py

   Lines 103 to 108 in 84f4e4f

   	width = 1280
   	height = 720
   	fx = 920.003
   	fy = 919.888
   	cx = 640.124
   	cy = 358.495

color_stream = profile.get_stream(realsense.stream.color);
color_video_stream = rgb_stream .as('video_stream_profile');
color_intrinsic=depth_aligned_to_color_intrinsic = color_video_stream.get_intrinsic()

在这个项目中，假设您在记录数据时已经通过 pyrealsense 对齐了深度和颜色帧（示例），因此不需要外部参数。只有相机的内在参数用于创建点云。
Hello, I have a question . What does the pose matrix in this function, that is “volume.integrate(rgbd, cameraIntrinsics, camera_poses[num_cam_pose].pose) ”，the external parameter matrix of the camera（camera_poses[num_cam_pose].pose）, represent? Does it mean that the camera to the world (in other words ： the coordinates under the camera coordinate system=posethe coordinates under the world coordinate system ) or the world to the camera (the coordinates under the world coordinate system=posethe coordinates under the camera coordinate system )? I hope you can solve my doubts，thanks.

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Hi @jly0810 !
I'm looking into this project again, so I wondered: have you succeeded with adjusting the code to your data?
If not, I could try to run it for you, if you share several (3-5) color+depth frames and camera intrinsics.

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Hello mam,
I captured the data to reconstruct a model. Firstly, I kept the sensor constant and moved the object. At that time it gave descent results. But, On doing the vice-versa process it was not giving the accurate results. Which, means In the final output it was producing multiple instances of object.
Can you suggest better way to do scanning while the object keep constant.

Using these registration method can we reconstruct the bigger objects like car , bike , and any other large objects.

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@ethyd4 Hello!
You should create a new issue - in this branch it's offtopic.
In the new issue please explain what "producing multiple instances of object" means - on what stage of the algorithm it happens and how does it look.

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