Closed BostonLobster closed 3 years ago
Most likely, you will only need to modify the path_example.yaml
. The syntax is:
datasets:
"your_scene_name":
scene_path: your_scene/your_scene_config.yaml # path to your scene config -- needs to be filled in separately
target_path: your_scene/images_undistorted # path to undistorted images of your scene
target_name_func: "lambda i: f'{i}.png'" # lambda which defines the file name format for ground truth picture #i in target_path
You'll also need to create your_scene/your_scene_config.yaml
mentioned above. The structure is:
viewport_size: [2160, 3840] # width, height of your target images
intrinsic_matrix: path/to/scene/intrinsic.txt
view_matrix: path/to/scene/view_matrices.txt
pointcloud: path/to/scene/point_cloud.ply
You can look at some examples by downloading the sample data provided with the repository. Hope this helps.
@seva100 Thanks for your reply!
I figure out how to achieve that now. But I have another question: in the readme, the metashape_build_cloud.py
produces a point_cloud.obj
, but in the sample data, the pointcloud file is pointcloud.ply
, is there any script to convert between them?
Each ScanNet scene contains RGB-D images, so I can project 2D pixels to 3D point cloud and save them to a
.ply
file. But how to modifypath_example.yaml
andtrain_example.yaml
to fit the descriptors on thisply
file?Any guidelines or suggestions?