MVP-Nav让机器人只用普通摄像头就能安全导航，它用3D基础模型理解空间，比之前纯语义方法更安全。
MVP-Nav提出一种物理感知的RGB-only导航框架，利用3D基础模型将单目图像的2D语义实例投影为3D有向包围盒，重建显式物理占用空间。该方法通过多层价值图（MVM）将语义优先级与重建几何整合到共享成本空间，实现了语义推理与物理约束的统一。在零样本目标导航基准上，MVP-Nav显著优于现有无深度方法，达到最先进性能。
MVP-Nav: Multi-layer Value Map Planner Navigator
Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth information introduces severe physical uncertainty and semantic-physical misalignment. Existing approaches either rely on high-level semantic reasoning without geometric grounding or learn end-to-end policies that lack explicit physical constraints, often resulting in semantically plausible but physically unsafe behaviors. In this paper, we propose MVP-Nav, a physical-aware RGB-only navigation framework that aligns perception, planning, and control with the real 3D world. MVP-Nav reconstructs explicit physical occupancy from monocular observations by leveraging 3D foundation models to project 2D semantic instances into 3D oriented bounding boxes, forming a global spatial semantic representation. To unify high-level semantic reasoning and low-level physical constraints, we introduce a Multi-layer Value Map (MVM) that integrates semantic priorities and reconstructed geometry into a shared cost space, enabling physically grounded geometric planning. Extensive experiments on zero-shot object navigation benchmarks demonstrate that MVP-Nav significantly outperforms existing depth-free methods, achieving state-of-the-art performance and validating that structured physical priors can effectively compensate for the absence of active depth sensors.