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import json
import os
import datasets
_CITATION = """\
@InProceedings{...},
title = {Your Dataset Title},
author={Your Name},
year={2025}
}
"""
_DESCRIPTION = """\
Dataset containing multi-view images with camera poses, depth maps, and masks for NeRF training.
"""
_LICENSE = "MIT"
class RefRefConfig(datasets.BuilderConfig):
"""BuilderConfig for RefRef dataset."""
def __init__(self, scene=None, **kwargs):
"""BuilderConfig for RefRef dataset.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(**kwargs)
self.scene = scene
class RefRef(datasets.GeneratorBasedBuilder):
"""A dataset loader for NeRF-style data with camera poses, depth maps, and masks."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = RefRefConfig
BUILDER_CONFIGS = [
RefRefConfig(
name="single_non_convex",
description="Single non-convex scene configuration for RefRef dataset.",
),
RefRefConfig(
name="multiple_non_convex",
description="Multiple non-convex scene configuration for RefRef dataset.",
),
RefRefConfig(
name="single_convex",
description="Single convex scene configuration for RefRef dataset.",
)
]
def _info(self):
features = datasets.Features({
"image": datasets.Image(),
"depth": datasets.Image(),
"mask": datasets.Image(),
"transform_matrix": datasets.Sequence(
datasets.Sequence(datasets.Value("float64"), length=4),
length=4
),
"rotation": datasets.Value("float32")
})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage="",
license=_LICENSE,
citation=_CITATION
)
def _split_generators(self, dl_manager):
# Automatically find all JSON files matching the split patterns
return [
datasets.SplitGenerator(
name=cat,
gen_kwargs={
"filepaths": os.path.join(f"https://huggingface.co/datasets/yinyue27/RefRef/resolve/main/image_data/{cat}/{self.config.name}/{self.config.scene}"),
"split": cat
},
) for cat in ["textured_sphere_scene", "textured_cube_scene"]
]
def _generate_examples(self, filepaths, split):
for split in ["train", "val", "test"]:
split_filepaths = os.path.join(filepaths, f"transforms_{split}.json")
with open(split_filepaths, "r", encoding="utf-8") as f:
try:
data = json.load(f)
except json.JSONDecodeError:
print("error")
scene_name = os.path.basename(os.path.dirname(split_filepaths))
for frame_idx, frame in enumerate(data.get("frames", [])):
base_dir = os.path.dirname(split_filepaths)
yield f"{scene_name}_{frame_idx}", {
"image": os.path.join(base_dir, frame["file_path"]+".png"),
"depth": os.path.join(base_dir, frame["depth_file_path"]),
"mask": os.path.join(base_dir, frame["mask_file_path"]),
"transform_matrix": frame["transform_matrix"],
"rotation": frame.get("rotation", 0.0)
} |