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78d2329 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import pathlib
import torch
from optgs.dataset.dataset_colmap import Parser
import json
from optgs.scripts.convert_dl3dv_utils import load_metadata
from einops import rearrange, repeat
if __name__ == '__main__':
scene = "14eb48a50e37df548894ab6d8cd628a21dae14bbe6c462e894616fc5962e6c49"
colmap_cache_dir = pathlib.Path("datasets/dl3dv-colmap-cache/1K")
colmap_benchmark_dir = pathlib.Path("datasets/dl3dv-benchmark")
chunk_path = pathlib.Path("datasets/dl3dv-480p-chunks/test/000004.torch")
# Extract points and cameras from colmap cache
parser_colmap_cache = Parser(
data_dir=str(colmap_cache_dir / scene),
factor=1, # not used for point cloud
normalize=False, # not used for point cloud
load_images=False, # not used for point cloud
dl3dv_settings=None
)
c2w_colmap_cache = torch.from_numpy(parser_colmap_cache.camtoworlds)
points_xyz_colmap_cache = torch.from_numpy(parser_colmap_cache.points)
# Load colmap cache transform
with open(colmap_cache_dir / scene / "transforms.json", 'r') as f:
transform_colmap_cache_data = json.load(f)
transforms_colmap_c2ws = []
for frame in transform_colmap_cache_data['frames']:
c2w = torch.tensor(frame['transform_matrix'], dtype=c2w_colmap_cache.dtype)
transforms_colmap_c2ws.append(c2w)
transforms_colmap_c2ws = torch.stack(transforms_colmap_c2ws, dim=0)
# Extract points and cameras from colmap benchmark
# images.bin is missing, so we do not have the poses of colmap from the benchmark
parser_benchmark = Parser(
data_dir=str(colmap_benchmark_dir / scene / "nerfstudio" / "colmap"),
# The sparse dir is not in the same hyrarchy of the images, for debugging we need to copy the spase dir one step up
factor=1, # not used for point cloud
normalize=False, # not used for point cloud
load_images=False, # not used for point cloud
dl3dv_settings=None
)
c2w_benchmark = torch.from_numpy(parser_benchmark.camtoworlds)
points_xyz_benchmark = torch.from_numpy(parser_benchmark.points)
# Load transforms.json from nerfstudio format
with open(colmap_benchmark_dir / scene / "nerfstudio" / "transforms.json", 'r') as f:
transforms_benchmark_data = json.load(f)
transforms_benchmark_c2ws = []
for frame in transforms_benchmark_data['frames']:
c2w = torch.tensor(frame['transform_matrix'], dtype=c2w_colmap_cache.dtype)
transforms_benchmark_c2ws.append(c2w)
transforms_benchmark_c2ws = torch.stack(transforms_benchmark_c2ws, dim=0)
applied_transform = torch.tensor(transforms_benchmark_data["applied_transform"],
dtype=c2w_colmap_cache.dtype) # [3, 4]
# Loading chunk example cameras
chunk = torch.load(chunk_path)
chunk = chunk[0]
assert chunk["url"] == scene
cameras = chunk["cameras"]
w2c = repeat(torch.eye(4, dtype=c2w_colmap_cache.dtype),
"h w -> b h w", b=len(cameras)).clone()
w2c[:, :3] = rearrange(cameras[:, 6:], "b (h w) -> b h w", h=3, w=4)
c2w_chunk = w2c.inverse()
blender2opencv = torch.tensor(
[[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]],
dtype=c2w_colmap_cache.dtype,
device=c2w_colmap_cache.device
)
c2w_chunk_transformed = c2w_chunk @ blender2opencv
# Compare c2w_chunk_transformed with transforms_colmap_cache_c2ws
diff = c2w_chunk_transformed - transforms_benchmark_c2ws
max_diff = diff.abs().max()
print(f"Max difference between chunk poses and benchmark colmap poses: {max_diff.item()}")
assert max_diff < 1e-4, "Chunk camera poses do not match benchmark colmap poses after transformation."
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