metadata
license: mit
library_name: pytorch
tags:
- pointllm
- point-cloud
- 3d
- multimodal
- chain-of-thought
- reasoning
base_model: RunsenXu/PointLLM_7B_v1.2
datasets:
- QileXu/PoCoTI-55K
language:
- en
PointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought
Chaoqi Chen¹*, Qile Xu¹*, Wenjun Zhou¹, Hui Huang¹†
¹Shenzhen University *Equal contribution †Corresponding author
Paper | Project Page | Code | Collection
Official model weights for the paper PointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought (SIGGRAPH 2026).
PointLLM-R-7B is a 3D multimodal LLM fine-tuned from PointLLM on the PoCoTI-55K dataset, which augments point-cloud QA pairs with structured 5-step chain-of-thought reasoning.
Links
- 📄 Paper: https://arxiv.org/abs/2605.22013
- 🌐 Project page: https://vcc.tech/research/2026/PointLLM-R
- 💻 Code: https://github.com/Xqle/PointLLM-R
- 📦 Collection: https://huggingface.co/collections/QileXu/pointllm-r
- 🗂️ Training data: QileXu/PoCoTI-55K
- 📊 Eval GT: QileXu/OmniObject3D_brief_description_val_GT
Quick Start
See the GitHub repository for installation, inference, and evaluation instructions.
Citation
@inproceedings{chen2026pointllmr,
title = {PointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought},
author = {Chen, Chaoqi and Xu, Qile and Zhou, Wenjun and Huang, Hui},
booktitle = {ACM SIGGRAPH},
year = {2026},
pages = {}
}
License
MIT. The base model and Objaverse-derived data retain their original licenses.