Comments (3)
Hello, if you are interested in implementing depth-based warping I strongly recommend using the following code:
Line 110 in f7bf598
It takes a FrameData object, converts it to a pointcloud, and later renders to a new view (basically a classic forward depth-based warping).
The key function to look for is get_rgbd_pointcloud which implements the correct way of unprojecting points from a depth map to a point cloud. https://github.com/facebookresearch/pytorch3d/blob/a393cccf6ae95f7510371c1dafe42d403a2b1d24/pytorch3d/implicitron/tools/point_cloud_utils.py#L23
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But to clarify, the scale is consistent for both point clouds and the corresponding cameras.
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Thanks for the clarifications! After spending some time with the code yesterday, I indeed found the dbir_utils
quite useful.
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Related Issues (20)
- About loading data of multiple categories
- Problems with training original NeRF using CO3D dataset HOT 1
- Incorrect frame-to-sequence labelling, or image files, in dataset HOT 5
- How to generate 3d file, using the dataset?
- Quick question about the get_rgbd_point_cloud function HOT 2
- Which categories do you use in Table 3. of your paper? HOT 2
- Release raw video data?
- Bugs in foreground masks HOT 3
- absolute scale? HOT 1
- Units of the pointclouds HOT 1
- fewview_train subset JSON contain frames that belong in both of train and test sets HOT 2
- Any documents of v2? HOT 3
- Camera focal length for the test set different from dev set HOT 2
- Is it possible tp provide User ID for each sequence?
- Is it possible to download subset with depth maps, and extrinsincs HOT 3
- get_eval_batches_map returns wrong shape of results HOT 1
- Statistics for v2 HOT 2
- Wrong depth mask in the dataset HOT 8
- Pure black frames in single sequence dataset HOT 1
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