Improve model card: Add pipeline tag, abstract, usage example, and links (#1)

- Improve model card: Add pipeline tag, abstract, usage example, and links (8250e67a278f8b187638a2b1f9999e6ac9372990)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,9 +1,52 @@
 ---
 license: cc-by-nc-4.0
+pipeline_tag: depth-estimation
 ---
 
-This repository contains the camera depth model of the paper Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots.
+# Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots
 
-Model inference guide: https://github.com/ByteDance-Seed/manip-as-in-sim-suite/tree/main/cdm
+This repository contains the Camera Depth Model (CDM) introduced in the paper [Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots](https://huggingface.co/papers/2509.02530).
 
-Project page: https://manipulation-as-in-simulation.github.io
+**Project page**: https://manipulation-as-in-simulation.github.io/
+**Code repository**: https://github.com/ByteDance-Seed/manip-as-in-sim-suite
+
+## Abstract
+Modern robotic manipulation primarily relies on visual observations in a 2D color space for skill learning but suffers from poor generalization. In contrast, humans, living in a 3D world, depend more on physical properties-such as distance, size, and shape-than on texture when interacting with objects. Since such 3D geometric information can be acquired from widely available depth cameras, it appears feasible to endow robots with similar perceptual capabilities. Our pilot study found that using depth cameras for manipulation is challenging, primarily due to their limited accuracy and susceptibility to various types of noise. In this work, we propose Camera Depth Models (CDMs) as a simple plugin on daily-use depth cameras, which take RGB images and raw depth signals as input and output denoised, accurate metric depth. To achieve this, we develop a neural data engine that generates high-quality paired data from simulation by modeling a depth camera's noise pattern. Our results show that CDMs achieve nearly simulation-level accuracy in depth prediction, effectively bridging the sim-to-real gap for manipulation tasks. Notably, our experiments demonstrate, for the first time, that a policy trained on raw simulated depth, without the need for adding noise or real-world fine-tuning, generalizes seamlessly to real-world robots on two challenging long-horizon tasks involving articulated, reflective, and slender objects, with little to no performance degradation. We hope our findings will inspire future research in utilizing simulation data and 3D information in general robot policies.
+
+## Usage (Camera Depth Models - CDM)
+
+To run depth inference on RGB-D camera data, follow these steps from the [CDM inference guide](https://github.com/ByteDance-Seed/manip-as-in-sim-suite/tree/main/cdm) in the GitHub repository:
+
+First, clone the repository and install the CDM package:
+
+```bash
+git clone https://github.com/ByteDance-Seed/manip-as-in-sim-suite.git
+cd manip-as-in-sim-suite/cdm
+pip install -e .
+```
+
+Then, navigate to the `cdm` directory and run inference using `infer.py`:
+
+```bash
+cd cdm
+python infer.py \
+    --encoder vitl \
+    --model-path /path/to/model.pth \
+    --rgb-image /path/to/rgb.jpg \
+    --depth-image /path/to/depth.png \
+    --output result.png
+```
+
+## Citation
+If you use this work in your research, please cite:
+
+```bibtex
+@article{liu2025manipulation,
+  title={Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots},
+  author={Liu, Minghuan and Zhu, Zhengbang and Han, Xiaoshen and Hu, Peng and Lin, Haotong and 
+          Li, Xinyao and Chen, Jingxiao and Xu, Jiafeng and Yang, Yichu and Lin, Yunfeng and 
+          Li, Xinghang and Yu, Yong and Zhang, Weinan and Kong, Tao and Kang, Bingyi},
+  journal={arXiv preprint},
+  year={2025}
+}
+```