Add pipeline tag and improve model card

README.md

@@ -1,3 +1,7 @@
+---
+pipeline_tag: image-to-3d
+---
+
 <h1 align="center">
 UniSHARP:<br>
 Universal Sharp Monocular View Synthesis
@@ -6,196 +10,49 @@ Universal Sharp Monocular View Synthesis
 <p align="center">
   <b>Meixi Song</b><sup>1</sup> ·
   <b>Dizhe Zhang</b><sup>1,*</sup> ·
-  <b>Hao Ren</b><sup>1</sup> ·
-  <b>Ruiyang Zhang</b><sup>1</sup> ·
-  <b>Bo Du</b><sup>2</sup> ·
-  <b>Ming-Hsuan Yang</b><sup>3</sup> ·
-  <b>Lu Qi</b><sup>1,2,*</sup>
+  <b>Hao Ren</b><sup>1,2</sup> ·
+  <b>Ruiyang Zhang</b><sup>1,3</sup> ·
+  <b>Bo Du</b><sup>4</sup> ·
+  <b>Ming-Hsuan Yang</b><sup>5</sup> ·
+  <b>Lu Qi</b><sup>1,4,*</sup>
   <br>
-  <sup>1</sup>Insta360 Research · <sup>2</sup>Wuhan University · <sup>3</sup>University of California, Merced
+  <sup>1</sup>Insta360 Research · <sup>2</sup>Sun Yat-sen University · <sup>3</sup>Beihang University · <sup>4</sup>Wuhan University · <sup>5</sup>University of California, Merced
 </p>
 
 <p align="center">
-  <img src="https://img.shields.io/badge/arXiv-coming_soon-red?logo=arxiv&logoColor=white" alt="arXiv">
+  <a href="https://huggingface.co/papers/2606.07514"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
   <a href="https://insta360-research-team.github.io/Unisharp-website/"><img src="https://img.shields.io/badge/Project_Page-Website-green" alt="Project Page"></a>
   <a href="https://huggingface.co/spaces/Insta360-Research/UniSHARP"><img src="https://img.shields.io/badge/HuggingFace-Demo-yellow" alt="Demo"></a>
   <a href="https://huggingface.co/datasets/Insta360-Research/OmniRooms"><img src="https://img.shields.io/badge/HuggingFace-OmniRooms-orange" alt="Dataset"></a>
   <a href="https://github.com/Insta360-Research-Team/UniSHARP"><img src="https://img.shields.io/badge/GitHub-Code-blue?logo=github&logoColor=white" alt="GitHub"></a>
 </p>
 
-
 UniSHARP extends SHARP-style photorealistic monocular view synthesis to universal camera systems. Given a single image from a perspective, wide-FoV, fisheye, or panoramic camera, UniSHARP predicts a 3D Gaussian representation and renders high-quality novel views.
 
-<p align="center">
-  <img src="assets/teaser.gif" alt="UniSHARP teaser" width="90%">
-</p>
+This is the official implementation of the paper [UniSHARP: Universal Sharp Monocular View Synthesis](https://huggingface.co/papers/2606.07514).
 
 <p align="center">
-  <img src="assets/unisharp.png" alt="UniSHARP method" width="90%">
+  <img src="assets/teaser.gif" width="59.4%" alt="UniSHARP teaser">
+  <img src="assets/teaser2.gif" width="39.6%" alt="UniSHARP teaser 2">
 </p>
 
 ## 🔨 Installation
 
-Clone this repository and enter the project directory:
-
 ```bash
 git clone https://github.com/Insta360-Research-Team/UniSHARP.git
 cd Unisharp
-```
 
-Create a fresh conda environment:
-
-```bash
 conda create -n unisharp python=3.12 -y
 conda activate unisharp
-```
 
-Install PyTorch for your CUDA version. The code was smoke-tested with PyTorch 2.8 and torchvision 0.23:
-
-```bash
 pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0
-```
-
-Install the remaining Python dependencies:
-
-```bash
 pip install -r requirements.txt
 ```
 
-## 🧩 External Dependencies
-
-### UniK3D
-
-UniSHARP uses UniK3D for universal camera ray and feature prediction. Clone the official repository into `Unisharp/UniK3D`:
-
-```bash
-git clone https://github.com/lpiccinelli-eth/UniK3D.git UniK3D
-```
-
-### 3DGEER
-
-Fisheye rendering depends on the GEER CUDA rasterizer from 3DGEER. Clone the repository into `Unisharp/3dgeer`:
-
-```bash
-git clone https://github.com/boschresearch/3dgeer.git 3dgeer
-```
-
-If you only use perspective or panoramic inference, the GEER rasterizer may not be needed. It is required for fisheye rendering paths.
-
-## 🖼️ Dataset
-
-The released dataset is hosted on Hugging Face:
+## 📒 Sample Usage
 
-- Dataset: [Insta360-Research/OmniRooms](https://huggingface.co/datasets/Insta360-Research/OmniRooms)
-- Training manifests: [Insta360-Research/OmniRooms/manifests/train](https://huggingface.co/datasets/Insta360-Research/OmniRooms/tree/main/manifests/train)
-- Validation manifests: [Insta360-Research/OmniRooms/manifests/validation](https://huggingface.co/datasets/Insta360-Research/OmniRooms/tree/main/manifests/validation)
-
-**OmniRooms** is a panoramic simulation dataset highly suitable for 3D reconstruction, especially for 3DGS tasks. It consists of 16 large indoor scenes, each containing multiple rooms, and 300k RGB images covering both small and large pose movements with corresponding depth information. OmniRooms is collected via AirSim, with **OmniRooms-Wide** derived by projecting these panoramas into 130-degree equidistant fisheye views. For each anchor point on a 0.5 m voxel grid, we render one central camera and 29 cameras randomly sampled within a local axis-aligned 30 cm cube centered on the source camera. To isolate translation-induced synthesis, all cameras share a fixed orientation. Each frame is rendered as a 1024 x 2048 ERP image.
-
-<table>
-  <tr>
-    <td width="25%"><img src="assets/dataset/AIUE5_vol8_03_2x2.jpg" alt="OmniRooms scene AIUE5 vol8 03"></td>
-    <td width="25%"><img src="assets/dataset/AIUE5_vol8_04_2x2.jpg" alt="OmniRooms scene AIUE5 vol8 04"></td>
-    <td width="25%"><img src="assets/dataset/AIUE5_vol8_05_2x2.jpg" alt="OmniRooms scene AIUE5 vol8 05"></td>
-    <td width="25%"><img src="assets/dataset/AIUE_V01_001_2x2.jpg" alt="OmniRooms scene AIUE V01 001"></td>
-  </tr>
-  <tr>
-    <td width="25%"><img src="assets/dataset/AIUE_V01_003_2x2.jpg" alt="OmniRooms scene AIUE V01 003"></td>
-    <td width="25%"><img src="assets/dataset/AIUE_V01_004_2x2.jpg" alt="OmniRooms scene AIUE V01 004"></td>
-    <td width="25%"><img src="assets/dataset/AIUE_V02_001_2x2.jpg" alt="OmniRooms scene AIUE V02 001"></td>
-    <td width="25%"><img src="assets/dataset/AI_vol3_01_2x2.jpg" alt="OmniRooms scene AI vol3 01"></td>
-  </tr>
-  <tr>
-    <td width="25%"><img src="assets/dataset/AI_vol3_02_2x2.jpg" alt="OmniRooms scene AI vol3 02"></td>
-    <td width="25%"><img src="assets/dataset/AI_vol3_03_2x2.jpg" alt="OmniRooms scene AI vol3 03"></td>
-    <td width="25%"><img src="assets/dataset/AI_vol3_04_2x2.jpg" alt="OmniRooms scene AI vol3 04"></td>
-    <td width="25%"><img src="assets/dataset/AI_vol4_01_2x2.jpg" alt="OmniRooms scene AI vol4 01"></td>
-  </tr>
-  <tr>
-    <td width="25%"><img src="assets/dataset/AI_vol4_02_2x2.jpg" alt="OmniRooms scene AI vol4 02"></td>
-    <td width="25%"><img src="assets/dataset/AI_vol4_03_2x2.jpg" alt="OmniRooms scene AI vol4 03"></td>
-    <td width="25%"><img src="assets/dataset/AI_vol4_04_2x2.jpg" alt="OmniRooms scene AI vol4 04"></td>
-    <td width="25%"><img src="assets/dataset/AI_vol4_05_2x2.jpg" alt="OmniRooms scene AI vol4 05"></td>
-  </tr>
-</table>
-
-The code supports the following data sources and manifest aliases:
-
-- `RealEstate10K`
-- `HM3D`
-- `OmniRooms`
-- `OmniRooms-Wide`
-- `WildRGB-D`
-- `DL3DV` 
-- `ScanNet++ Fisheye`
-- `Replica`, and `Tanks and Temples` for validation-only protocols
-
-Training manifests use the names released under `manifests/train`:
-
-```text
-dataset_manifests/
-├── re10k_train_chunks.txt            
-├── hm3d_train_scenes.txt            
-├── omnirooms.txt              
-├── wildrgbd_train_scenes.txt         
-├── dl3dv_train_scenes.txt            
-└── scanetpp_fisheye_train_scenes.txt 
-```
-
-Validation manifests use the names released under `manifests/validation`:
-
-```text
-validation_manifests/
-├── re10k.txt                      
-├── dl3dv.txt                         
-├── hm3d.txt                          
-├── omnirooms.txt                      
-├── omnirooms_wide.txt              
-├── wildrgbd.txt                     
-├── scanetpp_fisheye.txt              
-├── replica.txt                       
-├── tat.txt                           
-```
-
-## 🤝 Checkpoints
-
-Training starts UniSHARP heads from scratch and loads the original pretrained UniK3D weights through the UniK3D loader. The official launcher does not resume from a previous UniSHARP checkpoint by default.
-
-Released UniSHARP checkpoints are available at [Insta360-Research/Unisharp](https://huggingface.co/Insta360-Research/Unisharp/tree/main). Place a checkpoint anywhere on disk and pass the path to validation or inference:
-
-```bash
-CHECKPOINT=/path/to/pretained_model.pt
-```
-
-## 🚀 Training
-
-Use the official gt-override training launcher:
-
-```bash
-bash scripts/train.sh
-```
-
-Training outputs are saved under:
-
-```text
-outputs/<run_name>/
-├── config.json
-├── losses.csv
-├── step_XXXXXXX.pt
-└── vis/
-```
-
-## 📊 Validation
-
-Run validation with a checkpoint:
-
-```bash
-bash scripts/validate_unisharp.sh /path/to/step_XXXXXXX.pt
-```
-
-## 📒 Inference
-
-Run single-image inference:
+### Single-image Inference
+Run inference to predict a 3D Gaussian representation and render views:
 
 ```bash
 python scripts/infer_unisharp.py \
@@ -204,83 +61,19 @@ python scripts/infer_unisharp.py \
   --out-dir outputs/inference
 ```
 
-Run a directory or image list:
+If calibrated camera parameters are available, you can pass them through a JSON file. Without it, the script predicts rays and fits parameters automatically.
 
-```bash
-python scripts/infer_unisharp.py \
-  --checkpoint /path/to/step_XXXXXXX.pt \
-  --image-dir /path/to/images \
-  --out-dir outputs/inference
-```
-
-If calibrated camera parameters are available, pass them through a JSON file. Without this file, the script predicts rays with UniK3D and fits the camera parameters automatically.
-
-Example perspective camera JSON:
-
-```json
-{
-  "camera": "perspective",
-  "intrinsics": {
-    "fx": 820.0,
-    "fy": 820.0,
-    "cx": 512.0,
-    "cy": 384.0
-  }
-}
-```
-
-```bash
-python scripts/infer_unisharp.py \
-  --checkpoint /path/to/step_XXXXXXX.pt \
-  --image /path/to/perspective.jpg \
-  --camera-json /path/to/perspective_camera.json
-```
-
-Example Fisheye624 camera JSON:
-
-```json
-{
-  "camera": "fisheye",
-  "camera_params": [820.0, 820.0, 512.0, 384.0, 0.01, -0.001, 0.0, 0.0]
-}
-```
-
-```bash
-python scripts/infer_unisharp.py \
-  --checkpoint /path/to/step_XXXXXXX.pt \
-  --image /path/to/fisheye.jpg \
-  --camera-json /path/to/fisheye_camera.json
-```
-
-For batched inference, the JSON can also contain per-image entries:
-
-```json
-{
-  "default": {
-    "camera": "perspective",
-    "intrinsics": [820.0, 820.0, 512.0, 384.0]
-  },
-  "images": {
-    "panorama.jpg": {
-      "camera": "panorama"
-    },
-    "fisheye.jpg": {
-      "camera": "fisheye",
-      "camera_params": [820.0, 820.0, 512.0, 384.0, 0.01, -0.001, 0.0, 0.0]
-    }
-  }
-}
-```
+## 🖼️ Dataset
 
+The model was trained on **OmniRooms**, a panoramic simulation dataset suitable for 3D reconstruction and 3DGS tasks. It is available on Hugging Face: [Insta360-Research/OmniRooms](https://huggingface.co/datasets/Insta360-Research/OmniRooms).
 
 ## 🙏 Acknowledgement
 
 This project builds on open-source work from:
-
-- [SHARP](https://github.com/apple/ml-sharp) for monocular Gaussian view synthesis
-- [UniK3D](https://github.com/lpiccinelli-eth/UniK3D) for universal camera geometry and features
-- [3DGEER](https://github.com/boschresearch/3dgeer) for generic-camera Gaussian rasterization
-- [gsplat](https://github.com/nerfstudio-project/gsplat) for Gaussian splatting utilities
+- [SHARP](https://github.com/apple/ml-sharp)
+- [UniK3D](https://github.com/lpiccinelli-eth/UniK3D)
+- [3DGEER](https://github.com/boschresearch/3dgeer)
+- [gsplat](https://github.com/nerfstudio-project/gsplat)
 
 ## 📝 Citation
 
@@ -291,4 +84,4 @@ This project builds on open-source work from:
   journal={arXiv},
   year={2026}
 }
-```
+```