DepthLM: Metric Depth From Vision Language Models
Authors: name: Zhipeng Cai, name: Ching-Feng Yeh, name: Hu Xu, name: Zhuang Liu, name: Gregory Meyer, name: Xinjie Lei, name: Changsheng Zhao, name: Shang-Wen Li, name: Vikas Chandra, name: Yangyang Shi
Organization: AI at Meta

Vision language models (VLMs) can flexibly address various vision tasks
through text interactions. Although successful in semantic understanding,
state-of-the-art VLMs including GPT-5 still struggle in understanding 3D from
2D inputs. On the other hand, expert pure vision models achieve super-human
accuracy in metric depth estimation, a key 3D understanding task. However, they
require task-specific architectures and losses. Such difference motivates us to
ask: Can VLMs reach expert-level accuracy without architecture or loss change?
We take per-pixel metric depth estimation as the representative task and show
that the answer is yes! Surprisingly, comprehensive analysis shows that
text-based supervised-finetuning with sparse labels is sufficient for VLMs to
unlock strong 3D understanding, no dense prediction head or complex
regression/regularization loss is needed. The bottleneck for VLMs lies actually
in pixel reference and cross-dataset camera ambiguity, which we address through
visual prompting and intrinsic-conditioned augmentation. With much smaller
models, our method DepthLM surpasses the accuracy of most advanced VLMs by over
2x, making VLMs for the first time comparable with pure vision models.
Interestingly, without explicit enforcement during training, VLMs trained with
DepthLM naturally avoids over-smoothing, having much fewer flying points at
boundary regions than pure vision models. The simplicity of DepthLM also
enables a single VLM to cover various 3D tasks beyond metric depth. Our code
and model will be released at the link below.