3 citations · 5 across the 7 of their papers we have counts for
7 papers
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Jinfeng Xu, Xianzhi Li, Yuan Tang +5
Recent advancements in deep learning have greatly enhanced 3D object recognition, but most models are limited to closed-set scenarios, unable to handle unknown samples in real-worl…
MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning
Xu Han, Yuan Tang, Jinfeng Xu +1
We introduce Monarch Sparse Tuning (MoST), the first reparameterization-based parameter-efficient fine-tuning (PEFT) method tailored for 3D representation learning. Unlike existing…
Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation
Qiao Yu, Xianzhi Li, Yuan Tang +4
Generating 3D meshes from a single image is an important but ill-posed task. Existing methods mainly adopt 2D multiview diffusion models to generate intermediate multiview images,…
More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding
Yuan Tang, Xu Han, Xianzhi Li +5
Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs…
PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud
Qiao Yu, Xianzhi Li, Yuan Tang +4
Faithfully reconstructing textured meshes is crucial for many applications. Compared to text or image modalities, leveraging 3D colored point clouds as input (colored-PC-to-mesh) o…
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Yuan Tang, Xu Han, Xianzhi Li +4
Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language Models (LLMs) with images using a simple projector. Inspired by their success…
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
Xu Han, Yuan Tang, Zhaoxuan Wang +1
Existing Transformer-based models for point cloud analysis suffer from quadratic complexity, leading to compromised point cloud resolution and information loss. In contrast, the ne…