Fix project page URL: prs-eth.github.io/PaGeR (404) -> pager360.github.io

README.md

@@ -18,7 +18,7 @@ pipeline_tag: depth-estimation
 <a title="Github" href="https://github.com/prs-eth/PaGeR" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
     <img src="https://img.shields.io/github/stars/prs-eth/PaGeR?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="Github">
 </a>
-<a title="Website" href="https://prs-eth.github.io/PaGeR/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+<a title="Website" href="https://pager360.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
     <img src="https://img.shields.io/badge/%E2%99%A5%20Project%20-Website-blue" alt="Website">
 </a>
 <a title="arXiv" href="https://arxiv.org/abs/2605.26368" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
@@ -56,7 +56,7 @@ So the unified PaGeR checkpoint emits *both* the SI depth map and the metric dep
 
 Indoor and outdoor scenes are served by twin scale heads, so a single checkpoint covers both regimes. The active head can be selected manually or routed automatically — see [Model Details](#model-details) below for how that routing is done at inference time.
 
-You can also browse the rest of our [PaGeR HF collection](https://huggingface.co/collections/prs-eth/pager) or try the [interactive demo](https://huggingface.co/spaces/prs-eth/PaGeR).
+You can also browse the rest of our [PaGeR HF collection](https://huggingface.co/collections/prs-eth/pager-697241d06b3733a6f18e4d39) or try the [interactive demo](https://huggingface.co/spaces/prs-eth/PaGeR).
 
 ## Model Details
 
@@ -72,6 +72,7 @@ You can also browse the rest of our [PaGeR HF collection](https://huggingface.co
 - **Indoor / outdoor routing (inference-time add-on, not part of the paper):** The paper's per-domain numbers were produced with the indoor or outdoor scale head selected manually per dataset. For convenience in the released demo and CLI, a small zero-shot [CLIP ViT-B/32](https://github.com/openai/CLIP) classifier (loaded via [`open_clip`](https://github.com/mlfoundations/open_clip)) can auto-pick between the twin scale heads at inference time, by scoring the 4 equatorial cubemap faces against two text-prompt centroids ("indoor scene" vs. "outdoor scene"). The router lives outside the checkpoint and can be overridden by the user (`--scene_mode {auto,indoor,outdoor}`).
 - **Resolution:** Designed for high-resolution ERP inputs, up to 3K.
 - **License:** [CC BY-NC 4.0](LICENSE) — academic / non-commercial use only. The released weights are derivative works of the [Depth Anything 3](https://github.com/ByteDance-Seed/Depth-Anything-3) `da3-giant` backbone, released by ByteDance under CC BY-NC 4.0, and inherit that restriction. The CLIP ViT-B/32 weights used for the indoor/outdoor router are loaded from [`open_clip`](https://github.com/mlfoundations/open_clip) at runtime; they are MIT-licensed and travel separately, so they do not propagate any additional restriction. Commercial use is not permitted.
+- **Resources for more information:** [Project Website](https://pager360.github.io/), [Paper](https://arxiv.org/abs/2605.26368), [Code](https://github.com/prs-eth/PaGeR).
 
 ### Other released checkpoints