Grounding natural language questions to functionally relevant regions in 3D objects -- termed language-driven 3D affordance grounding -- is essential for embodied intelligence and human-AI interaction. Existing methods, while progressing from label-based to language-driven approaches, still face challenges in open-vocabulary generalization, fine-grained geometric alignment, and part-level semantic consistency. To address these issues, we propose a novel two-stage cross-modal framework that enhances both semantic and geometric representations for open-vocabulary 3D affordance grounding. In the first stage, large language models generate part-aware instructions to recover missing semantics, enabling the model to link semantically similar affordances. In the second stage, we introduce two key components: Affordance Prototype Aggregation (APA), which captures cross-object geometric consistency for each affordance, and Intra-Object Relational Modeling (IORM), which refines geometric differentiation within objects to support precise semantic alignment. We validate the effectiveness of our method through extensive experiments on a newly introduced benchmark, as well as two existing benchmarks, demonstrating superior performance in comparison with existing methods.
VoxAfford: Multi-Scale Voxel-Token Fusion for Open-Vocabulary 3D Affordance Detection
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人机混合增强智能国家级实验室，人工智能与机器人研究所，西安交通大学）
Open-Vocabulary Affordance Detection in 3D Point Clouds
FPT Software AI Center（FPT软件人工智能中心）；VNUHCM-University of Science（越南胡志明市国家大学-科学大学）；Automation & Control Institute (ACIN), TU Wien（维也纳工业大学自动化与控制研究所）；Center for Vision, Automation & Control, AIT Austrian Institute of Technology (GmbH)（奥地利技术研究院（AIT）视觉、自动化与控制中心）；Ton Duc Thang University（孙德胜大学）；University of Arkansas（阿肯色大学）；University of Liverpool（利物浦大学）
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation
CMIC, Shanghai Jiao Tong University（上海交通大学CMIC）；Beijing Institute of Technology（北京理工大学）；National University of Defense Technology（国防科技大学）；CUHK（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）
RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（北京先进信息科学与网络技术重点实验室）；Wuhan University（武汉大学）；JD Explore Academy（京东探索研究院）；Sun Yat-sen University（中山大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；University of Electronic Science and Technology of China（电子科技大学）；Peng Cheng Laboratory（鹏城实验室）
SceneParser: Hierarchical Scene Parsing for Visual Semantics Understanding
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Zhongguancun Academy（中关村学院）；Huazhong University of Science and Technology（华中科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；South China University of Technology（华南理工大学）；University of Oxford（牛津大学）；Peking University（北京大学）
BLaDA: Bridging Language to Functional Dexterous Actions within 3DGS Fields
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）；Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University（教育部贵州大学先进制造技术重点实验室）
