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Allow passing fused point cloud in the training #161
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| lines = (line.strip() for line in fid) | ||
| lines = (line for line in lines if line and not line.startswith("#")) | ||
| # lines = (line for line in lines if line and not line.startswith("#")) | ||
| lines = (line for line in lines if line == "" or not line.startswith("#")) |
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Just curious, what does this line fix?
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Hi @wilsonCernWq, Thank you for your review.
This update handles cases in images.txt where no 2D points are associated with an image.
In such cases, some entries contain only:
IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME
and do not include the usual POINTS2D[] line (which normally lists (X, Y, POINT3D_ID)).
A line was added to properly handle these cases during file reading.
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In this case, would we remove line 517 (the comment) and add actual comments to it to explain a bit? Sorry for the trouble! Thank you!
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Sure, will do that. Thanks.
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Hi @wilsonCernWq I updated the following:
- Replaced “accumulated” with “fused” to avoid ambiguity.
- Added comments to clarify line 518.
Could you please take a look? Thank you! If everything looks good, could you help me merge it?
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wilsonCernWq
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Looks good to me, thanks for accommodating the change request
Summary
This merge request adds support for fused point cloud initialization .
Features Added
Fused Point Cloud Initialization
Introduced a new initialization method,
fused_point_cloud, enabling initialization of 3D Gaussians from a point cloud (.plyfile).cuSFM Output Adaptation
Modified the line parsing logic in function
read_colmap_extrinsics_textto handle cuSFM’simages.txtformat, where some images contain only the lineIMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAMEand do not include the
POINTS2D[]line (which normally contains(X, Y, POINT3D_ID)).Config Files Using Accumulated Point Cloud Initialization
Added
configs/apps/cusfm_3dgut_mcmc.yaml— configuration for accumulated point cloud–based training.Added
configs/initialization/accumulated_point_cloud.yaml— defines parameters for point cloud initialization:observation_scale_factor: 0.01use_observation_points: trueaccumulated_point_cloud_path: configurable path to the input.plyfileCommands to train 3DGRUT using a given point cloud for initialization