card: add local viser demo section

README.md

@@ -80,6 +80,23 @@ clean up masks (NMS/min-points), use the inference pipeline in the demo repo / S
 (`pipeline.py`, `clip_eval.py`, `text_encoder.py`, `postprocessing.py`, `labels.py`) —
 e.g. its `inference.py` CLI or the Gradio `app.py`.
 
+## Demo (run locally)
+
+A small local demo lives under [`demo/`](./demo) — no GPU cloud / HF Space needed, run it
+on your own machine (requires WarpConvNet with its compiled extension). It takes text
+class names, runs segmentation, and shows the result in an interactive 3D
+[**viser**](https://viser.studio) viewer:
+
+```bash
+pip install -r demo/requirements.txt          # + warpconvnet (compiled)
+python demo/demo_viser.py --port 8080         # uses a bundled sample point cloud
+# your own scene + vocabulary:
+python demo/demo_viser.py --ply my_scene.ply --class-names "chair" "table" "lamp" "other"
+```
+
+Open the printed `http://localhost:8080` — each predicted instance is a distinct color.
+A headless CLI (`demo/inference.py`) and a Gradio app (`demo/app.py`) are also included.
+
 ## Intended use & limitations
 
 - **Intended:** research on open-vocabulary 3D scene understanding; segmenting indoor RGB