195 citations · 272 across the 17 of their papers we have counts for
9 papers · 1 filter
Point Transformer V3: Simpler, Faster, Stronger
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang +6
This paper is not motivated to seek innovation within the attention mechanism. Instead, it focuses on overcoming the existing trade-offs between accuracy and efficiency within the…
DreamComposer: Controllable 3D Object Generation via Multi-View Conditions
Yunhan Yang, Yukun Huang, Xiaoyang Wu +5
Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of…
GPT4Point: A Unified Framework for Point-Language Understanding and Generation
Zhangyang Qi, Ye Fang, Zeyi Sun +5
Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting p…
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Haoyi Zhu, Honghui Yang, Xiaoyang Wu +8
In contrast to numerous NLP and 2D vision foundational models, learning a 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data va…
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
Honghui Yang, Sha Zhang, Di Huang +9
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown w…
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract
Bohao Peng, Zhuotao Tian, Xiaoyang Wu +4
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic featu…
SAM3D: Segment Anything in 3D Scenes
Yunhan Yang, Xiaoyang Wu, Tong He +2
In this work, we propose SAM3D, a novel framework that is able to predict masks in 3D point clouds by leveraging the Segment-Anything Model (SAM) in RGB images without further trai…
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
Bohao Peng, Zhuotao Tian, Xiaoyang Wu +4
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic featu…
Understanding Imbalanced Semantic Segmentation Through Neural Collapse
Zhisheng Zhong, Jiequan Cui, Yibo Yang +4
A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equi…