BridgeAfford tackles the query‑target gap in language‑guided 3D affordance grounding by aligning interaction regions across objects and refining queries with visual evidence. The framework introduces Interaction Region Alignment to promote cross‑object consistency of geometrically similar regions, and Visual Evidence Aggregation to guide queries toward correct regions using a 2D affordance model. Experiments on LASO and PIAD show that BridgeAfford outperforms existing methods, especially on unseen objects, demonstrating robust generalization.
By Yijie Su, Yakun Zhu, Yi Bin, Pengpeng Zeng, Liang Chen, Guoqing Wang, Jingkuan Song, Heng Tao Shen
arXiv:2608.20720v1 Announce Type: new
Abstract: Open-world 3D affordance grounding requires localizing functional object parts in 3D given free-form language queries. Existing methods typically assum...
By Junqi Wu, Kaihua Tang, Xuanwen Chen, Hongzhi Li, Jianqiang Huang, Xian-Sheng Hua
arXiv:2607. 03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception.
By Seung Il Lee, Qinqian Lei, Daguang Xu, Dong Yang, Robby T. Tan, Yixin Chen, Bo Wang
arXiv:2605.03927v3 Announce Type: replace
Abstract: Vision-language models have demonstrated strong performance across robotic perception and instruction-following tasks. However, they still struggle...
By Xiaowen Sun, Matthias Kerzel, Mengdi Li, Xufeng Zhao, Paul Striker, Stefan Wermter
3D-SceneQ is a new 3D large language model that tackles two key limitations of existing 3D scene understanding pipelines: the inclusion of irrelevant object tokens that add noise, and the exclusive use of point cloud data that lacks rich semantic cues. It introduces a Query‑Guided Adaptive Pruning (QGAP) module that selectively keeps task‑relevant objects based on user instructions, and a Multi‑modal Object‑level Feature Enhancement (MOFE) module that fuses 2D feature embeddings into the 3D representation to enrich semantic information. Experiments show that 3D‑SceneQ surpasses state‑of‑the‑art methods on several benchmarks, achieving significant gains in metrics such as CIDEr and EM@1.
By Yongqi Shan, Lu Zhang, Jiazuo Yu, Yunzhi Zhuge, Huchuan Lu
arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.
By Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang
Vision-language perception has achieved impressive progress in aligning natural language with visual observations, yet grounding high-level semantics into part-level physical interaction remains chall...
UniRAP is a unified model that infers part-level physical affordances and translates language instructions into actionable geometric representations. It treats the task as a conditional multimodal generation problem, integrating visual inputs, textual instructions, and optional prompts into a shared spatiotemporal representation via a unified interface token mechanism. The model employs a Unified Affordance Decoder to jointly perform object detection, part-level affordance segmentation, and 4‑DoF interaction pose estimation, and uses a curriculum-based transfer training strategy to improve data efficiency with limited textual annotations.
By Linfei Li, Ruining Hu, Lin Zhang, Zhong Wang, Fengyi Zhang, Ying Shen, Binqiang Wang, Xin Zhang
arXiv:2602. 19710v3 Announce Type: replace-cross Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-specific action supervision.
By Haitao Lin, Hanyang Yu, Jingshun Huang, He Zhang, Yonggen Ling, Ping Tan, Xiangyang Xue, Yanwei Fu
arXiv:2609.37264v1 Announce Type: cross
Abstract: Affordance perception aims to localize actionable regions supporting embodied interaction, yet 2D and 3D affordance grounding have evolved as separat...
By Yuhao Liu, Yiming Zhong, Hanqing Wang, Shaocheng Yan, Yuhang Zhang, Wenzhou Lyu, Ziyang Ding, Wei Zhang, Xue Zhao, Jin Pan, Yuexin Ma, Xinge Zhu
arXiv:2610.20610v1 Announce Type: new
Abstract: Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches e...
By Jahyeok Koo, Hyeonseo Yu, Seungho Jang, Jaewoo Jung, Jiwon Kang, Seokju Cho, Seungryong Kim
arXiv:2610.02117v1 Announce Type: cross
Abstract: On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a froze...
By Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc, Nicolas Thome, Spyros Gidaris