DepthVLM enables vision-language models to predict dense depth maps

Researchers have developed DepthVLM, a new framework that enables Vision-Language Models (VLMs) to predict dense metric depth maps from single images. Unlike previous methods that relied on external models or inefficient per-pixel queries, DepthVLM integrates a lightweight depth head directly into the VLM backbone. This approach allows for the generation of full-resolution depth maps alongside language outputs in a single forward pass, improving both efficiency and 3D understanding capabilities. The framework also introduces a unified indoor-outdoor metric depth benchmark and demonstrates superior performance compared to existing VLMs and pure vision models. AI

影响 Enhances 3D understanding in VLMs, potentially leading to more capable multimodal foundation models.

排序理由 The cluster contains a research paper detailing a new framework for dense metric depth estimation in VLMs. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• arXiv
• DepthVLM
• Vision-Language Models

• 论文
• 模型发布

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →