When training on the Replica and ScanNet datasets, I found that using the datasets' built-in trajectories yielded high-quality meshes. However, replacing them with COLMAP output resulted in poorly reconstructed and incomplete meshes. I attempted to normalize the primary axis in the COLMAP poses, scaling the other two axes by the same factor, but the outcome remained chaotic. Additionally, I observed that adjusting the parameter “3” in the formula scale = 2. / (np.max(max_vertices - min_vertices) + 3.) influenced the results. Could you explain the purpose of this parameter? Do you have any suggestions for effectively incorporating COLMAP trajectories into the training process?
When training on the Replica and ScanNet datasets, I found that using the datasets' built-in trajectories yielded high-quality meshes. However, replacing them with COLMAP output resulted in poorly reconstructed and incomplete meshes. I attempted to normalize the primary axis in the COLMAP poses, scaling the other two axes by the same factor, but the outcome remained chaotic. Additionally, I observed that adjusting the parameter “3” in the formula scale = 2. / (np.max(max_vertices - min_vertices) + 3.) influenced the results. Could you explain the purpose of this parameter? Do you have any suggestions for effectively incorporating COLMAP trajectories into the training process?