Caesarrr/co3d_parquet

Probe CO3D Parquet Export

This directory contains a Parquet export of the probe-ready CO3D subset.

Summary

• Source layout: experiments/probe/datasets/co3d
• Export layout: experiments/probe/datasets/co3d_parquet
• Row unit: one sequence with exactly 8 selected frames
• Categories: 51
• Selected sequences: 4396
• Original on-disk sequences: 20273
• Valid sequences before per-category truncation: 15938
• Rows per shard: 64
• Shards: train=55, val=7, test=7

Column Overview

• Scalar metadata: split, category, sequence_name, camera_source, available_frame_count, selected_frame_count, quality_score, total_span_deg, max_slot_error_deg, rmse_slot_error_deg, sweep_deg, monotonicity
• Frame media columns: image_0..7, mask_0..7, depth_0..7, object_only_0..7
• Additional metadata: camera_poses_npz, selected_frames_json, trajectory_metrics_json, sequence_annotation_json, frame_annotations_json

Loading Example

from io import BytesIO
import json
import numpy as np
from datasets import load_dataset

ds = load_dataset(
    "parquet",
    data_files={
        "train": "train-*.parquet",
        "validation": "val-*.parquet",
        "test": "test-*.parquet",
    },
)

sample = ds["train"][0]
image0 = sample["image_0"]
mask0 = sample["mask_0"]
depth0 = sample["depth_0"]
object_only0 = sample["object_only_0"]

selected_frames = json.loads(sample["selected_frames_json"])
sequence_annotation = json.loads(sample["sequence_annotation_json"])
frame_annotations = json.loads(sample["frame_annotations_json"])
camera_poses = np.load(BytesIO(sample["camera_poses_npz"]))

Notes

• All media columns are embedded in the Parquet shards, so the export is self-contained.
• selected_frames_json is the exact per-sequence metadata produced by the probe builder.
• assets/ contains the category distribution figures copied from the source export.
• Verify the upstream dataset redistribution terms and set the final Hugging Face metadata before publishing.