Model card for concerto-tiny.pretrain.pointcept

A Concerto self-supervised pretraining model (joint 2D-3D representation encoder).

Non-commercial. These weights are released by 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): 4.9
  • Input channels: 9
• Paper: Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
• Converted from: Pointcept/Concerto (CC-BY-NC-4.0)
• Library: torch-pointcloud

Install

pip install torch-pointcloud

Usage

import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate

model, info = tp.create_model(
    "concerto-tiny.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

@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}
}

@software{dujardin2026pytorchpointcloud,
  author  = {Arthur Dujardin},
  title   = {PyTorch PointCloud},
  year    = {2026},
  doi     = {10.5281/zenodo.22159632},
  url     = {https://github.com/arthurdjn/pytorch-pointcloud},
}