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update demo

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LinZhuoChen
2026-04-16 18:53:26 +08:00
parent 843d9ec31d
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@@ -83,6 +83,32 @@ pip install -e ".[vis]"
# 🎬 Demo
Run `demo.py` for interactive 3D visualization via a browser-based [viser](https://github.com/nerfstudio-project/viser) viewer (default `http://localhost:8080`).
### Try the Example Scenes
We provide three example scenes in `example/` that you can run out of the box:
| Scene | Frames | Description |
|:---|:---|:---|
| `church` | 286 | Outdoor church with complex geometry |
| `oxford` | 320 | Oxford street-level walkthrough |
| `university4` | 324 | University campus outdoor scene |
```bash
# Church scene
python demo.py --model_path /path/to/checkpoint.pt \
--image_folder example/church --mask_sky
# Oxford scene with sky masking (outdoor)
python demo.py --model_path /path/to/checkpoint.pt \
--image_folder example/oxford --mask_sky
# University scene
python demo.py --model_path /path/to/checkpoint.pt \
--image_folder example/university4 --mask_sky
```
### Streaming Inference from Images
```bash
@@ -99,8 +125,7 @@ python demo.py --model_path /path/to/checkpoint.pt \
### Streaming with Keyframe Interval
Use `--keyframe_interval` to reduce KV cache memory by only keeping every N-th frame as a keyframe. Non-keyframe frames still produce predictions but are not stored in the cache. This is useful for long sequences
which excesses 320 frames.
Use `--keyframe_interval` to reduce KV cache memory by only keeping every N-th frame as a keyframe. Non-keyframe frames still produce predictions but are not stored in the cache. This is useful for long sequences which exceed 320 frames.
```bash
python demo.py --model_path /path/to/checkpoint.pt \
@@ -108,6 +133,7 @@ python demo.py --model_path /path/to/checkpoint.pt \
```
### Windowed Inference (for long sequences, >3000 frames)
```bash
python demo.py --model_path /path/to/checkpoint.pt \
--video_path video.mp4 --fps 10 \
@@ -137,7 +163,23 @@ python demo.py --model_path /path/to/checkpoint.pt \
--image_folder /path/to/images/ --mask_sky
```
Sky masks are cached in `<image_folder>_sky_masks/` so subsequent runs skip regeneration.
Sky masks are cached in `<image_folder>_sky_masks/` so subsequent runs skip regeneration. You can also specify a custom cache directory with `--sky_mask_dir`, or save side-by-side mask visualizations with `--sky_mask_visualization_dir`:
```bash
python demo.py --model_path /path/to/checkpoint.pt \
--image_folder /path/to/images/ --mask_sky \
--sky_mask_dir /path/to/cached_masks/ \
--sky_mask_visualization_dir /path/to/mask_viz/
```
### Visualization Options
| Argument | Default | Description |
|:---|:---|:---|
| `--port` | `8080` | Viser viewer port |
| `--conf_threshold` | `1.5` | Visibility threshold for filtering low-confidence points |
| `--point_size` | `0.00001` | Point cloud point size |
| `--downsample_factor` | `10` | Spatial downsampling for point cloud display |
### Without FlashInfer (SDPA fallback)