Scene-Q: Confidence-Aware Coarse-to-Fine Querying of 3D Scenes with Selective VLM Reasoning

arXiv:2609.20235 · cs.CV, cs.RO · Submitted 2026-07-28 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-07-28

Updated: 2026-07-28

Terminology

• Instance Segmentation — 21× in this paper · explained in 3 episodes
• Affordance — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Grounding — 4× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Annotation Ambiguity — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Vison Language Model — 1× in this paper
• World Model — 1× in this paper · explained in 7 episodes

Sources

• OpenMask3D: Open-Vocabulary 3D Instance Segmentation
• EVA-CLIP: Improved Training Techniques for CLIP at Scale
• CoCa: Contrastive Captioners are Image-Text Foundation Models
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Qwen2.5-VL Technical Report
• SAM3D: Segment Anything in 3D Scenes
• Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation
• High-Quality Entity Segmentation
• OPT: Open Pre-trained Transformer Language Models
• LLaMA: Open and Efficient Foundation Language Models
• Qwen Technical Report
• Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
• Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models