Textured 3D meshes specify object geometry and appearance, making them a natural query for finding a particular asset in an image. We introduce LocateAnyMesh, a vision-language model that uses the mesh itself as part of its prompt. A frozen 3D encoder maps colored surface points to a compact token sequence, which a learned connector places alongside image and instruction tokens. We evaluate two grounding models with PPAT mesh tokens: LAM-Select selects image-region proposals, while LAM-Direct predicts coordinates directly. Neither requires rendered query views or view-template matching. New objects require a single encoder pass and no object-specific fine-tuning. LAM-Select improves localization on the reported real-image suites and the official BOP YCB-V evaluation. Secondary experiments compare mesh encoders, and a LAM-Direct study isolates surface color. A single-seed LAM-Select continuation supplies pointing and mesh-only description evidence without replacing the reported localization model. The results support mesh tokens as a query representation, with remaining limitations in presence decisions, identity binding, proposal coverage, and descriptive fidelity.

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