r3m3c3/office_dataset_5

office_data_5

Synchronized recording from three sensors on one rig: a Prophesee EVK4 (IMX636) event camera, a FLIR Blackfly S Firefly frame camera, and an Intel RealSense D455 (color + depth). All three were captured on a single host, so every timestamp in this dataset shares one clock -- no cross-sensor sync step is needed, just match by nearest timestamp.

Configs / how to load

from datasets import load_dataset

events = load_dataset("r3m3c3/office_data_5", "events")              # 174646633 rows: t, x, y, p
firefly = load_dataset("r3m3c3/office_data_5", "firefly") # 659 images
rs_color = load_dataset("r3m3c3/office_data_5", "realsense_color")# 225 images
rs_depth = load_dataset("r3m3c3/office_data_5", "realsense_depth")# 231 images

Each image config yields rows with a decoded PIL image column plus a timestamp_ns column. events yields a flat table (Arrow/Parquet-backed, 12 shard(s)) with one row per event.

Timestamps

Every timestamp -- events' t column and every image config's timestamp_ns column -- is an int64 nanosecond Unix epoch timestamp, all on the same clock (the recording host's system clock; single-machine capture, no NTP/PTP sync needed). To align any two modalities, find the nearest timestamp.

Event timestamps: the sensor's own internal clock is a free-running counter with no absolute epoch reference of its own. This dataset anchors it to epoch time via a single offset computed from the first event packet's ROS header timestamp. Events' relative timing to each other is exact (decoded bit-for-bit from the sensor's raw EVT3 stream); the absolute anchor is accurate to within that one packet's capture latency (a few milliseconds) -- the same order of uncertainty the other sensors' own timestamps carry.

Minimal example: events around a given frame

import numpy as np
from datasets import load_dataset

events = load_dataset("r3m3c3/office_data_5", "events", split="train")
frames = load_dataset("r3m3c3/office_data_5", "firefly", split="train")

t0 = frames[100]["timestamp_ns"]
t_col = np.asarray(events["t"])           # loads the t column as one array
lo, hi = np.searchsorted(t_col, [t0 - 10_000_000, t0 + 10_000_000])  # +/- 10ms
window = events[lo:hi]                     # dict of x, y, p, t lists for that window

Calibration

See calibration.yaml for per-camera intrinsics (K, D, R, P). Each entry has a source field -- either ros_camera_info (published live by the driver) or supplement:... (the driver didn't publish real intrinsics; filled in from a separate calibration pass instead). Check this before trusting a camera's numbers.

Sensors

sensor	model	modality
events	Prophesee EVK4 (IMX636)	event stream (EVT3)
firefly	FLIR Blackfly S BFS-U3-63S4C	RGB frames (debayered)
realsense_color	Intel RealSense D455	RGB frames
realsense_depth	Intel RealSense D455	depth (16-bit, aligned to color)