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license: cc-by-nc-4.0
library_name: mlx
pipeline_tag: graph-ml
tags:
  - mlx
  - mlx-swift
  - 3d
  - point-cloud
  - self-supervised-learning
  - point-transformer-v3
base_model: Pointcept/Utonia

Utonia — MLX (safetensors) weights for Apple Silicon

MLX-converted weights for Utonia (Utonia: Toward One Encoder for All Point Clouds, arXiv:2603.03283) — a Point Transformer V3 (mode-3) encoder pretrained across indoor RGB-D, outdoor LiDAR, remote sensing, object CAD, and video-lifted point clouds.

These weights power mlx-swift-utonia, a numerically verified Swift/mlx-swift port that runs Utonia natively on Apple Silicon (macOS): full-resolution multi-million-point clouds, ScanNet-20 semantic segmentation, and PCA feature visualization.

Files

File	Contents
`utonia.safetensors`	Encoder weights (137,253,816 params, float32) — converted from `utonia.pth`
`utonia_config.json`	Model config extracted from the checkpoint (`enc_channels=(54,108,216,432,576)`, `enc_depths=(3,3,3,12,3)`, `enc_num_head=(3,6,12,24,32)`, 3D RoPE, 4 serialization curves)
`utonia_seg_head_sc.safetensors`	ScanNet-20 linear-probe segmentation head (`Linear(1386→20)`) — converted from `utonia_linear_prob_head_sc.pth`
`utonia_seg_head_sc_config.json`	Seg-head config
`*_manifest.txt`	Tensor name/shape manifests for both checkpoints

Changes from the original

Converted from the PyTorch checkpoints in Pointcept/Utonia (no retraining, no fine-tuning — the numbers are byte-identical modulo the format changes below):

.pth (pickled state dict) → .safetensors, float32.
Config dict extracted from the checkpoint into a standalone JSON.
Tensor values are unchanged; the Swift loader transposes the spconv SubMConv3d kernels from (C_out, k, k, k, C_in) to (k, k, k, C_in, C_out) at load time.

Numerical parity of the Swift port against the reference PyTorch implementation: bit-exact serialization, encoder relative error 0.27 % (fp32 GPU drift), 99.87 % semantic-segmentation argmax agreement.

Usage (mlx-swift)

import Utonia

let session = try UtoniaSession.load(SessionConfig(weightsDir: weightsDirURL))
let result = session.run(RawCloud(coord: coords, color: colors, normal: normals))

Or via the CLI from mlx-swift-utonia:

utonia-cli semseg --weights-dir weights --input scene/ --output segmented.ply
utonia-cli pca    --weights-dir weights --input scene/ --output features.ply

License

CC-BY-NC-4.0, inherited from the original Pointcept/Utonia weights — non-commercial use only. The mlx-swift-utonia code is licensed separately (see its repository); this restriction applies to the weights.

All credit for the model belongs to the Utonia authors (Pointcept / The University of Hong Kong and collaborators).

Citation

@misc{zhang2026utoniaencoderpointclouds,
      title={Utonia: Toward One Encoder for All Point Clouds},
      author={Yujia Zhang and Xiaoyang Wu and Yunhan Yang and Xianzhe Fan and Han Li and Yuechen Zhang and Zehao Huang and Naiyan Wang and Hengshuang Zhao},
      year={2026},
      eprint={2603.03283},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.03283},
}

@misc{pointcept2023,
    title={Pointcept: A Codebase for Point Cloud Perception Research},
    author={Pointcept Contributors},
    howpublished = {\url{https://github.com/Pointcept/Pointcept}},
    year={2023}
}