L1 ⚛ L1-096 ⊙ Testnet

Scanning LiDAR (pulsed direct Time-of-Flight)

Depth Imaging · Direct ToF ranging

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⬇ principle.md ⬇ principle.json

Fork L1-096

⬇ L1-096.md ⬇ L1-096.json

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You are helping me submit a MODIFICATION of L1-096 (Scanning LiDAR (pulsed direct Time-of-Flight)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-096.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-096.json so EVERY field matches the Markdown.
3. Keep the schema in the "File Mapping" footer at the bottom of the MD.
4. Keep the parent reference unchanged unless I ask otherwise.
Rules: the Markdown is the source of truth; use SI units; do NOT invent benchmark scores.
Output each file in its own fenced code block tagged with the filename.

Here is my template:
[PASTE THE .md HERE]

⚙ Forward Model

y = ∫dΩ `D.timestamp` `L.reflect_scene` `S.emit.pulse` x,    measurements ~ Poisson(αy)

world state x (3D point cloud)

→

S · emit · pulse `S.emit.pulse`

→

L · reflect scene `L.reflect_scene`

→

D · timestamp `D.timestamp`

→

Angular integration ∫dΩ

→

observation y (photon detector)

Noise: Poisson shot noise from quantum-limited detection

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-096.md

# ⚛ L1 Principle — Scanning LiDAR (pulsed direct Time-of-Flight) **ID:** `L1-096` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Depth Imaging — *Direct ToF ranging* > **🎯 Problem class:** linear inverse · **🧮 Solution space:** 3D point cloud > **📡 Carrier:** photon · **🌫 Noise:** shot poisson > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41547811 --- ## 🧠 1. Introduction **Scanning LiDAR (pulsed direct Time-of-Flight)** is a **linear inverse problem** whose unknown lives in **3D point cloud** space, within the **Direct ToF ranging** sub-domain of **Depth Imaging**. Measurements consist of photons collected by an optical detector via a **pulsed dtof scan** sensing mechanism. The forward operator applies, in order: S · emit · pulse operator; L · reflect scene operator; D · timestamp operator; integration over the solid angle of incidence/emission. Observations are corrupted by Poisson shot noise from quantum-limited detection. Well-posed when returned pulse SNR > 10 dB. Atmospheric attenuation alpha_atm(d) = exp(-gamma * d) cuts range quadratically at rain/fog; retroreflective surfaces saturate the receiver; motion distortion (rolling-shutter of scan) biases point cloud during ego-motion. ## ⚙ 2. Forward Model Physical chain: **x** → S · emit · pulse → L · reflect scene → D · timestamp → Angular integration → **y** (detector). ``` y = ∫dΩ `D.timestamp` `L.reflect_scene` `S.emit.pulse` x, measurements ~ Poisson(αy) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `S.emit.pulse` | S · emit · pulse operator | | `L.reflect_scene` | L · reflect scene operator | | `D.timestamp` | D · timestamp operator | | `int.angular` | Integration over the solid angle of incidence/emission | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Depth Imaging | | Sub domain | Direct ToF ranging | | Carrier | photon | | Problem class | linear_inverse | | Solution space | 3D_point_cloud | | Noise model | shot_poisson | | Integration axis | temporal | | Difficulty delta | 3 | | L dag | 3 | ## 📡 4. Measurement Model Well-posed when returned pulse SNR > 10 dB. Atmospheric attenuation alpha_atm(d) = exp(-gamma * d) cuts range quadratically at rain/fog; retroreflective surfaces saturate the receiver; motion distortion (rolling-shutter of scan) biases point cloud during ego-motion. | Metric | Value | |---|---| | Metric | point_cloud_RMSE_m | | Secondary | detection_recall_at_range | ## 📏 5. Operating Range (Ω) **Center problem class:** `pulsed_dtof_lidar` · **Forward operator:** `pulsed_scanning_lidar` **Center point:** | Parameter | Unit | Value | |---|---|---| | N ch | — | 64 | | F scan hz | Hz | 10 | | Lambda nm | nm | 905 | | Max range m | m | 200 | | Tau pulse ns | ns | 5 | | Motion distortion | — | 0 | | Photon count per beam | — | 500 | | Atmospheric extinction | — | 0 | | Retroreflector saturation | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N ch | — | 16 – 256 | | F scan hz | Hz | 5 – 30 | | Lambda nm | nm | 850 – 1550 | | Max range m | m | 30 – 500 | | Tau pulse ns | ns | 0.5 – 10 | | Beam walk error | — | 0.0 – 0.01 | | Motion distortion | — | 0.0 – 0.5 | | Photon count per beam | — | 10 – 5000 | | Atmospheric extinction | — | 0.0 – 2.0 | | Crosstalk neighbour beams | — | 0.0 – 0.05 | | Retroreflector saturation | — | 0.0 – 0.2 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.05 m point RMSE at 100 m | Metric | Range | |---|---| | Rmse m | 0.01 – 1.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **point_cloud_RMSE_m**, with κ = `500` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x1e40777b65932f4f482483408e2e456e7e6c6ec14eac5d24f72266a9b27b20be` - **Chain tx hash:** `0x9c61c56323795b056cd1503e8bea10e7f3dcfa9106f71743c4afc98efbcf7deb` - **Chain block:** `41547811` --- ## File Mapping This bundle consists of: `L1-096.md`, `L1-096.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-096.md` | Source of truth — edit this | Human or LLM | | `L1-096.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-096`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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L2-096-001 Scanning LiDAR Mismatch-Only Spec

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📋 JSON metadata

{
  "artifact_id": "L1-096",
  "chain_block": 41547811,
  "chain_hash": "0x1e40777b65932f4f482483408e2e456e7e6c6ec14eac5d24f72266a9b27b20be",
  "chain_tx_hash": "0x9c61c56323795b056cd1503e8bea10e7f3dcfa9106f71743c4afc98efbcf7deb",
  "domain": "Depth Imaging",
  "hardness_fn": {
    "delta": 3,
    "kappa": 500,
    "metric": "point_cloud_RMSE_m",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "point_cloud_RMSE_m",
    "regime": "Well-posed when returned pulse SNR \u003e 10 dB. Atmospheric attenuation alpha_atm(d) = exp(-gamma * d) cuts range quadratically at rain/fog; retroreflective surfaces saturate the receiver; motion distortion (rolling-shutter of scan) biases point cloud during ego-motion.",
    "secondary": "detection_recall_at_range"
  },
  "physics_fingerprint": {
    "L_DAG": 3.0,
    "carrier": "photon",
    "difficulty_delta": 3,
    "domain": "Depth Imaging",
    "integration_axis": "temporal",
    "noise_model": "shot_poisson",
    "primitives": [
      "S.emit.pulse",
      "L.reflect_scene",
      "D.timestamp",
      "int.angular"
    ],
    "problem_class": "linear_inverse",
    "sensing_mechanism": "pulsed_dtof_scan",
    "solution_space": "3D_point_cloud",
    "sub_domain": "Direct ToF ranging",
    "title": "Scanning LiDAR (pulsed direct Time-of-Flight)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "pulsed_scanning_lidar",
      "fmcw_lidar",
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    ],
    "allowed_omega_dimensions": [
      "N_ch",
      "f_scan_Hz",
      "tau_pulse_ns",
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      "photon_count_per_beam",
      "atmospheric_extinction",
      "motion_distortion",
      "retroreflector_saturation",
      "crosstalk_neighbour_beams",
      "beam_walk_error"
    ],
    "allowed_problem_classes": [
      "pulsed_dtof_lidar",
      "lidar_semantic_segmentation",
      "lidar_slam"
    ],
    "center_spec": {
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      "forward_operator": "pulsed_scanning_lidar",
      "input_format": "range_scan_plus_intensity",
      "omega": {
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        "atmospheric_extinction": 0.0,
        "f_scan_Hz": 10,
        "lambda_nm": 905,
        "max_range_m": 200,
        "motion_distortion": 0.0,
        "photon_count_per_beam": 500,
        "retroreflector_saturation": 0.0,
        "tau_pulse_ns": 5
      },
      "problem_class": "pulsed_dtof_lidar"
    },
    "epsilon_bounds": {
      "rmse_m": [
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        1.0
      ]
    },
    "omega_bounds": {
      "N_ch": [
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        256
      ],
      "atmospheric_extinction": [
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        2.0
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      "beam_walk_error": [
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      "crosstalk_neighbour_beams": [
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      ],
      "f_scan_Hz": [
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        30
      ],
      "lambda_nm": [
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        1550
      ],
      "max_range_m": [
        30,
        500
      ],
      "motion_distortion": [
        0.0,
        0.5
      ],
      "photon_count_per_beam": [
        10,
        5000
      ],
      "retroreflector_saturation": [
        0.0,
        0.2
      ],
      "tau_pulse_ns": [
        0.5,
        10
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Direct ToF ranging",
  "title": "Scanning LiDAR (pulsed direct Time-of-Flight)"
}

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