3 code implementations • 27 Feb 2024 • Zekun Qi, Runpei Dong, Shaochen Zhang, Haoran Geng, Chunrui Han, Zheng Ge, He Wang, Li Yi, Kaisheng Ma
This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
Ranked #1 on 3D Question Answering (3D-QA) on 3D MM-Vet
3D Point Cloud Linear Classification 3D Question Answering (3D-QA) +8
1 code implementation • 20 Sep 2023 • Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, HongYu Zhou, Haoran Wei, Xiangwen Kong, Xiangyu Zhang, Kaisheng Ma, Li Yi
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation.
Ranked #1 on Visual Question Answering on MMBench (GPT-3.5 score metric)
2 code implementations • NeurIPS 2023 • Zekun Qi, Muzhou Yu, Runpei Dong, Kaisheng Ma
VPP leverages structured voxel representation in the proposed Voxel Semantic Generator and the sparsity of unstructured point representation in the Point Upsampler, enabling efficient generation of multi-category objects.
1 code implementation • 31 May 2023 • Guofan Fan, Zekun Qi, Wenkai Shi, Kaisheng Ma
Geometry and color information provided by the point clouds are both crucial for 3D scene understanding.
Ranked #1 on Unsupervised 3D Semantic Segmentation on ScanNetV2
3 code implementations • 5 Feb 2023 • Zekun Qi, Runpei Dong, Guofan Fan, Zheng Ge, Xiangyu Zhang, Kaisheng Ma, Li Yi
This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
Ranked #1 on Zero-Shot Transfer 3D Point Cloud Classification on ModelNet10 (using extra training data)
3D Point Cloud Linear Classification Few-Shot 3D Point Cloud Classification +2
3 code implementations • 16 Dec 2022 • Runpei Dong, Zekun Qi, Linfeng Zhang, Junbo Zhang, Jianjian Sun, Zheng Ge, Li Yi, Kaisheng Ma
The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages.
Ranked #5 on Few-Shot 3D Point Cloud Classification on ModelNet40 10-way (10-shot) (using extra training data)
Few-Shot 3D Point Cloud Classification Knowledge Distillation +1