Upload concerto-large.pretrain.pointcept

README.md

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+---
+license: cc-by-nc-4.0
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- concerto
+- self-supervised
+---
+
+# Model card for concerto-large.pretrain.pointcept
+
+A Concerto self-supervised pretraining model (joint 2D-3D representation encoder).
+
+> **Non-commercial.** These weights are released by [Pointcept/Concerto](https://github.com/Pointcept/Concerto) under CC BY-NC 4.0 and may be used for research and evaluation only.
+
+## Model Details
+
+- **Model Type:** Self-supervised pretraining
+- **Model Stats:**
+  - Params (M): 207.7
+  - Input channels: 9
+- **Paper:** [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://arxiv.org/abs/2510.23607)
+- **Converted from:** [Pointcept/Concerto](https://github.com/Pointcept/Concerto) (CC-BY-NC-4.0)
+- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)
+
+## Install
+
+```bash
+pip install torch-pointcloud
+```
+
+## Usage
+
+```python
+import torch
+import torch_pointcloud as tp
+from torch_pointcloud.utils.data import collate
+
+model, info = tp.create_model(
+    "concerto-large.pretrain.pointcept",
+    task="base",
+    pretrained=True,
+    return_info=True,
+)
+model = model.cuda().eval()  # GPU-only kernels
+
+# synthetic sample with the keys a dataset provides
+num_points = 8192
+sample = {
+    "pos": torch.randn(num_points, 3),
+    "color": torch.rand(num_points, 3) * 255,
+    "normal": torch.randn(num_points, 3),
+    "segment": torch.zeros(num_points, dtype=torch.long),
+    "instance": torch.zeros(num_points, dtype=torch.long),
+}
+data = info["transform"](sample)
+data = collate([data])
+data = {key: value.cuda() for key, value in data.items()}
+
+with torch.no_grad():
+    out = model(data.get("x"), data["pos_grid"], data["batch"], pos=data["pos"])
+```
+
+## Citation
+
+```bibtex
+@article{concerto2025,
+  title   = {Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations},
+  author  = {Yujia Zhang and Xiaoyang Wu and Yixing Lao and Chengyao Wang and Zhuotao Tian and Naiyan Wang and Hengshuang Zhao},
+  journal = {arXiv preprint arXiv:2510.23607},
+  year    = {2025}
+}
+```

model.safetensors

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