195 citations · 272 across the 17 of their papers we have counts for
17 papers
DreamComposer++: Empowering Diffusion Models with Multi-View Conditions for 3D Content Generation
Yunhan Yang, Shuo Chen, Yukun Huang +6
Recent advancements in leveraging pre-trained 2D diffusion models achieve the generation of high-quality novel views from a single in-the-wild image. However, existing works face c…
SAMPart3D: Segment Any Part in 3D Objects
Yunhan Yang, Yukun Huang, Yuan-Chen Guo +5
3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harnes…
VIP: Vision Instructed Pre-training for Robotic Manipulation
Zhuoling Li, Liangliang Ren, Jinrong Yang +5
The effectiveness of scaling up training data in robotic manipulation is still limited. A primary challenge in manipulation is the tasks are diverse, and the trained policy would b…
Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images
Zhangyang Qi, Yunhan Yang, Mengchen Zhang +7
Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, deta…
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
Bohao Peng, Xiaoyang Wu, Li Jiang +4
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especia…
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
Francis Engelmann, Ayca Takmaz, Jonas Schult +25
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023. The goal of this wo…
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding
Chengyao Wang, Li Jiang, Xiaoyang Wu +4
Self-supervised 3D representation learning aims to learn effective representations from large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as t…
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…
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
Xiaoyang Wu, Yixing Lao, Li Jiang +2
As a pioneering work exploring transformer architecture for 3D point cloud understanding, Point Transformer achieves impressive results on multiple highly competitive benchmarks. I…