Hello mam
I seen your rgbd integration repository the dataset which you were taken is working perfectly. I took another dataset and tried to reconstruct it but is not giving the proper output. But whenever I'm using the online datasets in your respository it was giving the proper results.
I recorded the two rosbag files using ros, Consists of both the color and the depth data after that I extracted the rgb and the depth data from those files and then performed the 3d reconstruction It was not giving the proper results can I know why it was happening ?
can you provide the proper details how you were captured the data?

Excuse me, the following is the result of reconstruction using my dataset. The dataset includes three groups of RGBD images, but the reconstruction results do not coincide well. What is the reason? Is the camera in your sample dataset fixed, rotating only by people? Is this necessary?



The following is the original picture（RGB picture）：

I hope you can solve my problem. It has troubled me for a long time. Thank you！！

Hello mam,
While, Scanning the small objects it was showing some incorrect reconstruct results. Some of the reconstructed results were

python main__TSDF_Integrate__color_depth.py
Traceback (most recent call last):
File "main__TSDF_Integrate__color_depth.py", line 156, in
result_ransac = execute_global_registration(source_down, target_down,
File "main__TSDF_Integrate__color_depth.py", line 67, in execute_global_registration
result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
TypeError: registration_ransac_based_on_feature_matching(): incompatible function arguments. The following argument types are supported:
1. (source: open3d.cuda.pybind.geometry.PointCloud, target: open3d.cuda.pybind.geometry.PointCloud, source_feature: open3d::pipelines::registration::Feature, target_feature: open3d::pipelines::registration::Feature, mutual_filter: bool, max_correspondence_distance: float, estimation_method: open3d.cuda.pybind.pipelines.registration.TransformationEstimation = TransformationEstimationPointToPoint without scaling., ransac_n: int = 3, checkers: List[open3d.cuda.pybind.pipelines.registration.CorrespondenceChecker] = [], criteria: open3d.cuda.pybind.pipelines.registration.RANSACConvergenceCriteria = RANSACConvergenceCriteria class with max_iteration=100000, and confidence=9.990000e-01) -> open3d.cuda.pybind.pipelines.registration.RegistrationResult

Invoked with: PointCloud with 2923 points., PointCloud with 2580 points., Feature class with dimension = 33 and num = 2923
Access its data via data member., Feature class with dimension = 33 and num = 2580
Access its data via data member., 0.015, TransformationEstimationPointToPoint without scaling., 4, [CorrespondenceCheckerBasedOnEdgeLength with similarity_threshold=0.900000, CorrespondenceCheckerBasedOnDistance with distance_threshold=0.015000], RANSACConvergenceCriteria class with max_iteration=4000000, and confidence=1.000000e+00

Hello, I am a college student. I started learning 3D reconstruction recently. Your public 3D reconstruction project has given me great help and inspiration. I can't express my gratitude in words! And your dataset is complete. Can I have the honor to use your dataset to verify the algorithm and publish in my article? Looking forward to your reply!

Can u plz let me know, what data is stored in the depth.png files present inside this directory
RGBD-Integration-2020/Test_data/