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Dense point cloud
Depth point cloud
Depth & dense fusionDense point cloud
The Dense point cloud step generates a dense point cloud from images.
- Click Process
. - On the Menu bar, click Process > Dense point cloud...
It allows users to change the following processing options:
Algorithm
This option allows switching between the densification algorithms:
- Hardware accelerated (Default): The densification will be computed using a GPU-capable algorithm. This option can contribute to reduced processing time.
- Standard: The densification will be computed using the standard algorithm.
Image scale
Image Scale defines the scale of the images at which additional 3D points are computed.
- 1/1 (Original image size, Slow): The original image size is used to compute additional 3D points. This option require four times more RAM and processing time than when processing with the default value 1/2, and usually it does not significantly improve results.
- 1/2 (Half image size, Default): Half size images are used to compute additional 3D points. It is the recommended image scale.
- 1/4 (Quarter image size, Fast): Quarter size images are used to compute additional 3D points. Less points are computed than with the 1/2 image scale and the processing time is reduced. This option can generate more points when processing vegetation areas.
- 1/8 (Eighth image size, Tolerant): Eighth size images are used to compute additional 3D points. Less points are computed than with the 1/2 or 1/4 image scale and the processing time is further reduced. This option can generate more points when processing vegetation areas.
Multiscale
Multiscale (default): When this option is activated, additional 3D points are computed on multiple image scales, starting with the selected Image scale and going to the 1/8 image scale.
- Computing additional points in vegetation areas as well as keeping details in areas without vegetation.
- Filling holes in places with low texture, for example, uniform walls.
- The edges of buildings contain noise.
- When reconstructing thin elements, for example, the structure of the cell tower.
Density
This parameter defines the density of the point cloud:
- Optimal (Default): A 3D point is computed for every 8th pixel of the original image. This is the recommended point cloud density.
- High: A 3D point is computed for every 2nd pixel of the original image. It results in an oversampled point cloud that requires up to 4 times more processing time and RAM than Optimal density. Typically, this point cloud option does not significantly improve the results compared to Optimal density.
- Low: A 3D point is computed for every 32nd pixel of the original image. The final point cloud is computed up to 4 times faster and uses up to 4 times less RAM than Optimal density.
Minumum matches
Minimum Number of Matches (2-6, 3 default) represents the minimum number of valid re-projections of this 3D point to the images. For example, when using the Minimum Number of Matches 3 (default), each 3D point has to be correctly re-projected in at least 3 images.
- 2: For projects with a small overlap. Note that this option produces a point cloud with more noise and artifacts.
- 4, 5, or 6: To reduce the noise and improve the quality of the point cloud. Note that this option might compute less 3D points in the final point cloud.
Noise filter
Noise filter processing option provides a cleaner point clouds for datasets with oblique images.
It filters out points that are generated far away from images for example, features that are visible on images but are far on the horizon will not be reconstructed.
Sky filter
Sky filter processing option removes points in a dense point cloud associated with sky.
The feature is especially useful for datasets that contain sky in images, for example:
- Terrestrial datasets acquired with PIX4Dcatch.
- Inspection datasets acquired with PIX4Dscan.
Mask-aware
Masks can enhance the quality of dense point clouds and meshes by removing selected elements from the scene, effectively hiding them from 3D. Specify the layers to be used in the Dense point cloud and Mesh steps under the Mask-aware setting.
Reoptimization
Starting from version 1.69.0, PIX4Dmatic enables the option for Reoptimization of the Dense point cloud.
- In the menu bar, click Process > Dense point cloud.. > Reoptimization
- Or select the Reoptimization button
after enabling the Dense point cloud step and click Start.
Exports
From the Exports section, the following point clouds can be exported after processing the Densify and Mesh step:
- Dense point cloud (available after processing Densify step)
- Point cloud from mesh .laz (available after processing Mesh step)
Dense point cloud
To export the point cloud automatically during the Densify step:
- In Exports, select the Dense point cloud .laz option.
- (Optional) Click
and specify the desired path and filename.
- On the Menu tab, click File > Export dense point cloud .laz...
- Specify the desired path and filename.
- Click Save.
Pointcloud from mesh .laz
PIX4Dmatic allows the generation and export of point clouds from the mesh. The default Density (pts/m3) or a customized one can be selected while exporting the new point cloud from the mesh. The expected point cloud size and the total number of points are also displayed while exporting the point clouds. This way, a point cloud is generated even for areas that are not directly reconstructed and holes no longer exist in the reconstructed point cloud. The point cloud generated from the mesh is in .laz format.
