arXiv AI
Jun 30
GROW$^2$: Grounding Which and Where for Robot Tool Use
arXiv:2606. 30632v1 Announce Type: cross Abstract: Can the robot use a plate to cut a cake if no knife is available?
AffordAny: Open-World 3D Affordance Grounding from Monocular RGB Images via Vision-Language-Guided Geometric Reasoning
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arXiv Computer Vision
Sep 24
Pro-Bench: Prompt-Robust Open-Vocabulary Visual Grounding Across Real-World Heterogeneous Environments
arXiv:2609.27076v1 Announce Type: new Abstract: Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined perce...
arXiv AI
Oct 2
Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
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...
arXiv AI
Jul 8
OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding
arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.
arXiv AI
Jul 7
Token-Based Affordance Grounding with Large Vision-Language Models
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.
arXiv AI
Sep 30
UniAfford: Token-Routed Multitask Learning for Generalizable 2D-3D Affordance Perception
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...
arXiv Computer Vision
Sep 10
StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
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...
arXiv AI
Sep 16
SceneBench: A Hierarchical Benchmark for Vision-Language Understanding of 3D Scenes
arXiv:2609.16233v1 Announce Type: cross Abstract: Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current ben...
arXiv AI
Sep 10
GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting
GoDeep is an annotation‑free method for open‑vocabulary 3D scene understanding that uses a vision‑language model solely as a translator to generate structured, entity‑level descriptions of each image. These descriptions are projected and aggregated in a language‑only embedding space, eliminating the need for a 3D training corpus or domain‑specific encoder. The approach achieves competitive performance on ScanNet++ and a cultural heritage benchmark, accurately localizes out‑of‑vocabulary objects, and offers explainable, point‑level predictions.
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SceneBench: A Hierarchical Benchmark for Vision-Language Understanding of 3D Scenes
Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current benchmarks. First, 3D datasets often rely on point clo...
arXiv AI
Jul 8
Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction
arXiv:2607. 05568v1 Announce Type: cross Abstract: Representing 3D shapes as compact sets of geometric primitives is fundamental to robotics, simulation, and scene understanding.
arXiv AI
Jul 1
PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding
arXiv:2606. 31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions.