Comments (3)
added for real data training and it improves quality. As for synthetic data, it would require setting the same random color for the data and the network, which is not straightforward, so I'll leave it for the future...
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This is a valid point, I also observe that in Chair scene, a white part of the chair is predicted to be background, which causes drop in accuracy. I will try random bg color in training, and fixed color in testing.
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However, this only works when alpha channel is known, therefore only a little part of data (some Synthetic-NSVF data don't have alpha channel...) so I don't know if it's worth implementing at the moment. I'd like to work on more substantial part first. Let me know if you have some findings!
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Related Issues (20)
- vren HOT 2
- An error of configuration environment during cmake
- debug .cu
- Poor reconstruction with white background in real dataset
- How to calculate the bbox for a custom recored NSVF dataset?
- do you used NDC in llff?
- about show_gui.py
- Train Result
- Volume rendering gradient equation
- RayMarcher的backward是不必要的 HOT 3
- Ambient Occlusion (AO) using the ([Instant-NGP framework]
- Question for the occupancy grid code of Raymatching.cu HOT 1
- def nerf_matrix_to_ngp(pose, scale=0.33, offset=[0, 0, 0]): new_pose = np.array([ [pose[1, 0], -pose[1, 1], -pose[1, 2], pose[1, 3] * scale + offset[0]], [pose[2, 0], -pose[2, 1], -pose[2, 2], pose[2, 3] * scale + offset[1]], [pose[0, 0], -pose[0, 1], -pose[0, 2], pose[0, 3] * scale + offset[2]], [0, 0, 0, 1], ], dtype=np.float32) return new_pose。What is the purpose of the above operation in Instant-Ngp, and how to adjust it accordingly based on the camera pose of your own dataset
- Use COLMAP depth for additional supervised loss
- Optimize extrinsics HOT 1
- How can i get rays_d from xyzs?
- Question about the structure of network
- questions about --scale and N_max
- `Trainer.fit` stopped: `max_epochs=30` reached. HOT 1
- Zero samples got into RuntimeError
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