Datasets:
Tasks:
Image-to-3D
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
code
License:
| 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=f"{'cubeBg' if cat == 'textured_cube_scene' else 'sphereBg' if cat == 'textured_sphere_scene' else 'envMapBg'}_{'singleMatConvex' if cat == 'single-convex' else 'singleMatNonConvex' if cat == 'single-non-convex' else 'multiMatNonConvex'}_{self.config.scene}", | |
| gen_kwargs={ | |
| "filepaths": os.path.join(f"https://huggingface.co/datasets/yinyue27/RefRef/resolve/main/image_data/{cat}/{self.config.name}/", | |
| f"{self.config.scene}_sphere" if cat == "textured_sphere_scene" else self.config.scene), | |
| }, | |
| ) for cat in ["textured_sphere_scene", "textured_cube_scene", "environment_map_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 opening " + split_filepaths) | |
| continue | |
| 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) | |
| } |