Seek-and-View Reasoning for Multi-View Spatial Understanding

arXiv:2610.11810 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/q1xiangchen/Vantage

Terminology

• Benchmark — 29× in this paper · explained in 2 episodes
• Alignment — 19× in this paper · explained in 1 episode
• Accuracy — 12× in this paper · explained in 1 episode
• Foundation Model — 10× in this paper · explained in 3 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Core — 5× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Model-Agnostic — 4× in this paper · explained in 2 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Training-Free Framework — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Two-stage training — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Agentic Approaches — 1× in this paper · explained in 1 episode
• Dynamic Weighting — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• OmniView-Space: Reinforcing Spatial Reasoning via Multi-Perspective Spatial Mapping
• OpenView: Empowering MLLMs with Out-of-view VQA
• Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
• Grounded 3D-Aware Spatial Vision-Language Modeling
• Dense Reward for Multi-View 3D Reasoning with Global Maps and Local Views
• Don't Show Pixels, Show Cues: Unlocking Visual Tool Reasoning in Language Models via Perception Programs
• SpatialStack: Layered Geometry-Language Fusion for 3D VLM Spatial Reasoning
• Explain Before You Answer: A Survey on Compositional Visual Reasoning
• Enhancing MLLM Spatial Understanding via Active 3D Scene Exploration for Multi-Perspective Reasoning
• SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning
• World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning
• Thinking with Imagination: Agentic Visual Spatial Reasoning with World Simulators
• Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence
• How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning
• Think3D: Thinking with Space for Spatial Reasoning
• Depth Anything 3: Recovering the Visual Space from Any Views
• $\pi^3$: Permutation-Equivariant Visual Geometry Learning
• VGGT-$\Omega$
• G3T Up! Gravity Aligned Coordinate Frames Simplify Pointmap Processing
• Gemma 4 Technical Report

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