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Hi,
The segmented point clouds for training and testing contain respectively the following properties:
- [x, y, z, r, g, b, semantic, instance, visible, confidence];
- [x, y, z, r, g, b, vi…
wialb updated
2 months ago
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A nice effect can be achieved if the "base" color of points (e.g. from classification renderer or from attribute by ramp renderer) is combined with a shade of gray from another attribute - especially …
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Hello everyone, in our lab we are currently testing our OAK-D PRO camera to detect point clouds of cylindrical PVC pipes. However, we have noticed that, keeping a fixed distance from the camera (z-axi…
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if selection table selects all points in the point cloud, `ccPointCloud::createNewCloudFromVisibilitySelection` will just return the original point cloud reference, which is confusing since this funct…
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I'm trying to do cross-modal training between lidar and camera with this dataset. Therefore I project the labeled accumulated point clouds to the images and cut out the points that are out of the fov …
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This is a good job.
I have some questions while reading the paper and the code.
The paper says that it can generate dense point clouds, but I don’t see any direct generation of dense point clouds …
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Hello!
I've got a pretty huge scan (the DB file is almost 70 GB large) which I'm generating point clouds of using rtabmap-databaseViewer.
What I find is that when exporting with decimation level…
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## 🚀 Feature
`pytorch.ops.voxel_down_sample(input, voxel_size)` would be a function to downsample input point cloud into output point cloud with a voxel
NOTE: Please look at the existing list of I…
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Hi,
Thanks for kindly providing an unofficial implementation of the GaussRecon! I am very interested in this fast method that could replace PoissonRecon for oriented point clouds. However, when I…