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.
P$^3$-VLM: A Point-based Alternative for Grounded 3D Vision-Language Models
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arXiv Computer Vision
3d ago
Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning with Lightweight Annotations
arXiv:2506.17545v2 Announce Type: replace Abstract: Large language models are increasingly being applied to 3D scene understanding. However, existing 3D-aware LLMs largely operate as black boxes: the...
arXiv Computer Vision
Sep 7
Where to Look Matters: Learning Influential Views for VLM-based 3D Visual Grounding
IVSGround introduces a lightweight view selector that learns to choose the most informative camera views for vision‑language model (VLM) based 3D visual grounding, replacing heuristic view selection. The selector is trained via a two‑stage rejection sampling process that uses feedback from a reasoning VLM to generate supervision signals. Experiments on ScanRefer and NR3D demonstrate that IVSGround consistently improves grounding accuracy over existing zero‑shot pipelines, underscoring the importance of selecting where to look for effective 3D visual grounding.
arXiv AI
Jul 24
3D-Aware VLMs with Implicit and Explicit Geometries
arXiv:2607. 21595v1 Announce Type: cross Abstract: Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning.
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
Jun 15
3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding
arXiv:2603. 04976v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored.
arXiv Computer Vision
Oct 7
M3SunAgent: Monocular 3D Spatial Understanding Agent for Metric Depth Estimation and 3D Visual Grounding
arXiv:2610.07982v1 Announce Type: new Abstract: Monocular metric depth estimation and 3D visual grounding represent the two complementary cornerstones of monocular 3D spatial understanding (M3Sun), f...
arXiv Computer Vision
Aug 27
PointRL: Learning Point-Level Vision-Language Grounding from Verifiable Annotation Evidence
PointRL introduces a verifiable reinforcement learning framework that learns point-level vision‑language grounding from heterogeneous annotation evidence such as bounding boxes, masks, and instance labels. The method converts these annotations into pointing instructions while preserving target supports, instance membership, and set constraints as hidden verifier evidence, which a deterministic checker uses to score predictions. Evaluation on PointArena shows that PointRL improves Qwen3.5‑4B’s accuracy from 56.11% to 65.58%, and similar gains are observed on RoboSpatial, BLINK, and Ref‑Adv benchmarks.
arXiv Computer Vision
Aug 24
AffordAny: Open-World 3D Affordance Grounding from Monocular RGB Images via Vision-Language-Guided Geometric Reasoning
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...
arXiv Machine Learning
Sep 10
Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models
arXiv:2609.06880v1 Announce Type: cross Abstract: Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated pers...
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...
Hugging Face Trending Papers
Jul 21
ZeroSplat: Generalized Referring Segmentation in 3D Gaussian Splatting
Recent advancements in 3D Gaussian Splatting (3DGS) have enabled language-guided scene understanding. However, existing Referring 3D Gaussian Splatting (R3DGS) methods are fundamentally restricted to single-target queries.