6 citations · 14 across the 19 of their papers we have counts for
13 papers · 1 filter
RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation
Shaoxuan Wang, Guangting Zheng, Rui Huang +4
Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction. Yet prevalent diffusion- and flow-matching robot p…
ThinkAfford: Affordance-Centric Reasoning for Fine-Grained 3D Grounding in Cluttered Scenes
Xinrui Lin, Sha Zhang, Shumin Wang +3
Task-driven 3D affordance grounding aims to localize the functional region in a cluttered 3D scene that enables an action specified by a natural-language instruction. Existing meth…
LabBuilder: Protocol-Grounded 3D Layout Generation for Interactable and Safe Laboratory
Jianbao Cao, Zhangrui Zhao, Bohan Feng +15
Automated laboratories hold the promise of accelerating scientific discovery, yet their deployment is bottlenecked by the difficulty of designing safe and executable environments.…
GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction
Yedong Shen, Shiqi Zhang, Sha Zhang +6
Reconstructing static 3D scene from monocular video with dynamic objects is important for numerous applications such as virtual reality and autonomous driving. Current approaches t…
PGOV3D: Open-Vocabulary 3D Semantic Segmentation with Partial-to-Global Curriculum
Shiqi Zhang, Sha Zhang, Jiajun Deng +3
Existing open-vocabulary 3D semantic segmentation methods typically supervise 3D segmentation models by merging text-aligned features (e.g., CLIP) extracted from multi-view images…
Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving
Shumin Wang, Zhuoran Yang, Lidian Wang +7
The significant achievements of pre-trained models leveraging large volumes of data in the field of NLP and 2D vision inspire us to explore the potential of extensive data pre-trai…
LFP: Efficient and Accurate End-to-End Lane-Level Planning via Camera-LiDAR Fusion
Guoliang You, Xiaomeng Chu, Yifan Duan +4
Multi-modal systems enhance performance in autonomous driving but face inefficiencies due to indiscriminate processing within each modality. Additionally, the independent feature l…
Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures
Guoliang You, Xiaomeng Chu, Yifan Duan +6
When planning for autonomous driving, it is crucial to consider essential traffic elements such as lanes, intersections, traffic regulations, and dynamic agents. However, they are…
Agent3D-Zero: An Agent for Zero-shot 3D Understanding
Sha Zhang, Di Huang, Jiajun Deng +4
The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence. The current common practice is to finetune Large Language Mod…
HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation
Sha Zhang, Jiajun Deng, Lei Bai +3
We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network with a pre-trained image network in…
PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest
Jiajun Deng, Sha Zhang, Feras Dayoub +3
In this work, we present PoIFusion, a conceptually simple yet effective multi-modal 3D object detection framework to fuse the information of RGB images and LiDAR point clouds at th…
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…