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README.md

@@ -8,6 +8,7 @@ tags:
   - image-segmentation
   - semantic-segmentation
   - robotics
+  - edge-ai
   - construction-ai
   - autonomous-painting
   - wall-painting-robot
@@ -25,15 +26,15 @@ metrics:
   - mean-iou
   - iou
 model-index:
-  - name: painting-vision
+  - name: painting-vision-robotics-kit
     results: []
 ---
 
-# Painting Vision — robot-ready wall-paint coverage segmentation
+# Painting Vision Robotics Kit — robot-ready wall-paint coverage segmentation
 
-**A frontier research preview for autonomous painting and surface-finishing robots: from pixels to an executable paint plan.**
+**A lite, edge-deployable research preview for autonomous painting and surface-finishing robots: from pixels to an executable paint plan.**
 
-Painting Vision reads a single camera frame of a wall and returns far more than
+The Painting Vision Robotics Kit reads a single camera frame of a wall and returns far more than
 a mask. It segments the paintable surface, the fixtures and openings that must
 not be painted, and the substrate — then hands a downstream planner a *wall
 boundary, corners, a two-tool stroke plan, machine-readable waypoints, and a
@@ -47,6 +48,24 @@ the wall is, where the keep-outs are, and when the coat is complete**.
 > model weights are trained by the open `train.py` in this repo on
 > session-separated, real capture data.
 
+## Lite model, frontier lineage
+
+**This kit is the *lite* tier of our models.** The published
+`painting-vision-robotics-kit` is deliberately compact — a MobileNetV3/DeepLabV3-class
+backbone through SegFormer-B2 (`models.py --arch`) sized to run onboard a painting
+robot on CPU or a small accelerator, trading peak accuracy for latency, memory and
+offline operation (ONNX / TensorRT INT8 / CoreML targets; `docs/ROADMAP.md`, Phase 3).
+
+**Our frontier tier is the flagship.** Constructelligence is developing **frontier
+construction-AI models** — larger backbones, higher-resolution and multimodal (RGB +
+depth/LiDAR) input, trained on session-separated real capture. Those are a separate,
+larger model class and are **not** what this card publishes. What is open here is the
+*lite* variant: the same task and the same honest, testable pipeline, at a fraction of
+the size, runnable on a laptop today.
+
+Read this card as the **floor** of the range — a lite model shipped in the open by a
+frontier-model team — not the ceiling.
+
 ---
 
 ## Why this model exists
@@ -60,7 +79,7 @@ to know:
 - the **substrate** (e.g. drywall) so paint behaviour is not confused with surface type;
 - **remaining coverage** across repeated observations, so the robot knows when to stop.
 
-Painting Vision is designed end-to-end around those questions.
+The kit is designed end-to-end around those questions.
 
 ## Capabilities
 
@@ -109,6 +128,32 @@ wall/session leakage and thin classes, and recommend exactly how many more
 samples each gap needs. `train.py` supports class-, wall- and hybrid-balanced
 sampling so the balanced collection survives into the optimizer.
 
+### 6. Paint colour: named, matched, per face
+`paint_color.py` places the painted pixels in CIELAB, trims the darkest and
+brightest 10% (shadow, glare, lap marks) and names the coat ("magnolia
+#F1EBD6"), reports a k-means palette and a colour per measured wall face — so a
+face painted in the wrong colour or a missed second coat is visible in the
+report. A specified colour (`--reference-color`) is compared with **CIEDE2000**
+and banded (≤1 exact, ≤2 a touch-up match, ≤5 the same at a glance), and
+`safety_gate.py` stops the coat when the measured colour is not the specified
+one. The colour printed on a paint can's label is read and checked against the
+same reference, so the product on site is verified and not assumed.
+
+### 7. The equipment around the wall
+`site_objects.py` finds paint cans, trays, brushes, rollers, ladders and dust
+sheets around the wall, with no trained model (there is no annotated data for
+them): gradient outlines are cut above the frame's own noise, scene structure
+(skirting, ceiling, the wall/floor junction) is removed so a tin standing on the
+floor is not welded onto it, outlines are closed and filled back into bodies,
+and each body is scored per class from its own cues — aspect, fill, outline
+straightness, nap texture, the stripe a tin's rim or a brush's ferrule makes,
+the repeats tray ridges or ladder rungs make, frame position, colour. It answers
+what a site asks: is the gear the plan needs here, is anything standing where
+the paint is going (folded into the keep-out mask), and is the tin the right
+colour. Every detection carries the cues that fired and the scores of the
+classes it was *not* given; `safety_gate.py` warns by default on clutter and on
+missing tools, and stops on a colour mismatch.
+
 ## Quickstart
 
 ```sh
@@ -138,7 +183,9 @@ python3 depth_validate.py --depth depth.npy --intrinsics "fx,fy,cx,cy" \
 | --- | --- |
 | `models.py` | architecture factory: DeepLabV3 (MobileNet/ResNet) + SegFormer, semantic + drywall heads |
 | `train.py` | training, balanced sampling, window loss boost, boundary/coverage metrics |
-| `predict.py` | inference: masks, coverage, geometry, regularised windows |
+| `predict.py` | inference: masks, coverage, geometry, regularised windows, paint colour, equipment on site |
+| `paint_color.py` | the coat's colour named, paletted, per face, and matched with CIEDE2000 |
+| `site_objects.py` | model-free detection of paint cans, trays, brushes, rollers, ladders, dust sheets; the colour on a tin's label |
 | `robot_planner.py` | boundary/corners, two-tool stroke plan, waypoints, progress state |
 | `window_postprocess.py` | fragment merge + oriented rectangle fitting for windows |
 | `depth_validation.py` | LiDAR/depth plane fit, protrusion/recess checks, metric scale |
@@ -182,7 +229,7 @@ Always validate on held-out physical walls with the production camera.
 
 ```bibtex
 @misc{constructelligence_painting_vision,
-  title  = {Painting Vision: robot-ready wall-paint coverage segmentation},
+  title  = {Painting Vision Robotics Kit: robot-ready wall-paint coverage segmentation},
   author = {Constructelligence},
   year   = {2026},
   note   = {Research preview}