Stereo Carpet Extraction Data

It is first-person video of a person extracting and drying carpets: pre-spraying carpets, running a carpet extractor over the floor in passes and positioning air movers to dry it. It is recorded from their own point of view with two calibrated cameras, with hand pose and sensor streams alongside. The task is the one the site’s catalog lists in two rows. Stereo describes the capture, and MCAP describes the container. They are separate choices.

The site does not state that stereo is already running on these rows. The catalog lists a head-mounted phone or a multiview rig as the capture setup, and a stereo pair is a capture choice agreed in the skill spec and confirmed on a Spec Pilot. The data is human capture by paid, consented contributors, not robot or teleoperation data. It describes task video only and makes no cleanliness, drying-result, chemical, safety or certification claim. Firsthand is not affiliated with Foxglove or the MCAP maintainers.

Two, both in the Commercial Cleaning & Janitorial environment of the Facilities & Cleaning sector, and both rated Medium. "Carpet Extraction" is listed as Ready to deploy ("Pre-spray carpets, run the extractor over them in passes and set air movers to dry."). "Extract and dry carpets using a carpet extractor and air movers" is listed as Active collection ("Run the carpet extractor over the floor in passes and position air movers to dry it.").

Both list a head-mounted phone or a multiview rig as the capture setup. The catalog lists the environment examples as an office building, a shopping mall, an airport and an industrial facility, and does not say which building a row was captured in. The site says nothing further about what Active collection or Ready to deploy means in volume, hours or delivered stereo footage, so this page claims none. It names only the tools and items that appear in the rows: a carpet extractor, air movers, carpets and the floor. The first table below reproduces the two rows as the catalog states them.

The egocentric carpet extraction and floor stripping page covers four catalog rows, the two carpet extraction rows and the two strip-and-wax rows, and describes delivery in RLDS, LeRobot or WebDataset. The stereo floor stripping and waxing page gives stereo and MCAP treatment to the two strip-and-wax rows only, and does not cover the carpet extraction rows.

This page is the stereo and MCAP treatment of the two carpet extraction rows alone, with their own stream map and failure modes. It does not repeat the strip-and-wax steps. The comparison table below sets the pages side by side.

Carpet extraction is about where a tool meets the floor and in what order: which stretch has been pre-sprayed, which passes have been run, where an air mover has been positioned. A stereo pair recovers depth from the calibrated frame pair rather than from a standalone depth sensor, so the floor, the machine and the hands share a frame. The site gives no depth accuracy figure for these rows and no coverage, cleanliness or task-completion label, so what a buyer does with that depth is their pipeline’s question.

The site also states that depth degrades on wet and reflective surfaces and handles that with per-frame confidence maps shipped so a buyer can mask rather than guess. The rows end with drying, so that statement is the relevant one here, but how depth behaves on carpet, and on a uniform or low-texture floor, is Not stated, and a spec should check it on a Spec Pilot. Capture at your own facility is listed as not offered.

The site states 3D hand pose of 21 joints per hand with a per-joint visibility flag, and contact and grasp events that mark when fingers meet a tool or an item. In carpet extraction that is the hands on the extractor as it is run in passes, the hand that pre-sprays and the hands that position an air mover.

The site does not say whether the extractor, an air mover or the carpet counts as the object for contact events, so settle it in the spec. Wrist cameras give a closer view of the hands, and a multiview rig adds synchronized third-person angles when the spec asks for them.

The site describes two stereo setups and does not say which one a carpet extraction order uses. The hard-environments stack is a global-shutter 4K/60 head camera with a 120° lens, two 1080p/60 wrist cameras, a depth sensor at 848×480 and 30 fps, and 200 Hz 6-DoF IMUs. The off-the-shelf stereo head rig, dataset 06, is dual fisheye cameras at 1080p per eye, a 62 mm baseline, per-unit calibration, a 9-axis IMU and hand-tracking output.

Which rig, resolution and streams a carpet extraction order uses is Not stated on the site, and the third table below sets what is stated beside what is not. Intrinsics and extrinsics are re-solved at the start and end of every session, and streams are hardware-triggered onto one clock with a 2.0 ms cross-stream ceiling. Room size, lighting, machine type and camera clearance around the machine are Not stated, so any requirement is settled in the spec.

MCAP is an open source container file format for multimodal log data, supporting multiple channels of timestamped pre-serialized data. It is serialization-agnostic, so messages can be Protobuf, DDS (CDR), ROS, JSON and more. Its design is row-oriented and append-only, it stores schemas alongside the data, and it offers an optional index plus optional LZ4 or Zstandard chunk compression.

Reader and writer libraries exist for C++, Go, Python, Rust, Swift and TypeScript, and the mcap CLI can inspect, merge and split files. The foxglove/mcap repository is MIT-licensed; that is the MCAP project’s licence, not the licence of a Firsthand delivery. The MCAP hub page and the glossary entry cover the format in full.

Firsthand offers MCAP delivery, and dataset 06 is stated as shipping as MCAP. For carpet extraction the site states hand pose, a stereo-derived depth stream with confidence maps, IMU, contact events, action segments and per-frame instance masks elsewhere. What it does not state is which of those are channels in an MCAP file, how they are named, what encoding they use or at what rate.

No topic name, message schema or rate is stated on this page, and none should be assumed. Name the streams that matter in the skill spec and confirm them on a Spec Pilot batch. The fourth table below sets each stream beside what the site states about it.

Wet and low-texture surfaces are the first risk. The site states that depth degrades on wet and reflective surfaces, with per-frame confidence maps to mask low-confidence regions, and an episode is rejected when more than 35 percent of depth pixels in the manipulation region are invalid. How a stereo pair behaves on carpet, on a uniform floor or on a freshly extracted stretch is Not stated, and this page makes no claim about it.

Occlusion and blur are the second. The extractor or an air mover can hide the fingers that hold it. The visibility flag on each joint is the answer the site gives, contact events mark when a hand takes hold of an item, and a spec can ask for wrist views or a second angle so a grip the head camera loses is still recorded. The head camera is global-shutter, which avoids rolling-shutter skew, and the site notes that reach and grasp frames are where blur and occlusion show up.

Sequence is the third. The rows chain pre-spraying, running the extractor in passes and positioning air movers, so a spec naming each step and where it starts and ends gives a sequence that can be sliced by step, while a spec saying "extract the carpet" gives one long action segment. The rows do not say what one episode covers, so the spec decides. Machine brands, detergent or chemical names, dry times, certifications, facility or customer names and throughput are Not stated and are not claimed on this page.

Calibration is the fourth, because stereo depth is only as good as the rectification behind it: calibration is measured at the start and end of every session under a 0.28 px RMS gate. An episode is also rejected when the manipulated object is out of frame for more than 15 percent of it or when an undocumented bystander appears, and a reject log ships with every batch.

For every delivered episode the site states that faces, plates and screens are blurred irreversibly, and that footage that cannot be de-identified is discarded. The anonymization record ships with the data, and an undocumented bystander is a stated reject criterion.

The site does not state a rule specific to carpeted commercial spaces, a rule for who may be in the space while it is recorded, or how that differs between office, mall, airport and industrial settings, so this page makes no such claim. Settle it in the spec.

The October 2026 off-the-shelf catalog lists ~324,800 hours across 18 datasets, of which stereo is ~64,800 hours across 9 datasets. The site lists no off-the-shelf carpet extraction dataset, so stereo carpet extraction in MCAP is custom collection: custom collection at scale through a network of thousands of capture providers, for a consistent, recurring data flow to labs. The off-the-shelf stereo and hand-tracking video page shows what the rig delivers, and the custom order applies it to your task.

Where coverage is deep, the median time from signed spec to first validated batch is a median of 48 hours; where it is thin, turnaround runs three to four weeks. One of the two rows is Ready to deploy and one is Active collection, and the site does not say which speed applies to a stereo carpet extraction order, so the timeline is quoted against your spec. Collections are billed per validated hour, and rejected episodes and recaptures are never billed.

Describe the steps and where each starts and ends: pre-spraying carpets, running the extractor over the floor in passes and positioning air movers to dry it. Name the tools you expect to see, whether an episode covers one stretch of floor or a whole room, what counts as a reject and the variation your pipeline is missing. Then name the streams you want in the file, the topic naming and message encoding your reader expects, whether calibration ships inside the file or beside it, and whether MCAP is the only delivery or sits beside RLDS, LeRobot or WebDataset.

Most programs begin with a Spec Pilot, a small first batch that proves the spec and accept criteria on real footage before you scale. The fifth table below shows what each spec element decides.