Is your feature request related to a problem?
The Monocular Metric series currently ships only at Large scale (DA3METRIC-LARGE, 0.35B).
The any-view Main series already provides DA3-BASE (0.12B) and DA3-SMALL (0.08B), and
there's a DA3MONO-LARGE relative model — but all of the smaller checkpoints are relative-only.
There is no lightweight checkpoint that outputs real-world metric scale.
This is a blocker for real-time / on-device deployment. On edge runtimes — Unity Inference
Engine (Sentis), mobile, standalone XR headsets — the Small/Base models are the only ones that
run at interactive frame rates and are small enough to convert and quantize for the target,
but they're relative-only. The Large metric model is both too heavy for these targets and, in
practice, harder to get running in these runtimes. So any application needing absolute distance
on-device (AR/XR, robotics, assistive/accessibility tools) currently has to choose between
metric scale and a deployable footprint.
For context: Depth Anything V2 did release metric checkpoints at Small/Base/Large, so the move
to DA3 effectively removes the small-footprint metric option for edge users.
Describe the solution you'd like
Release metric checkpoints at the smaller scales — DA3Metric-Base and DA3Metric-Small — by
fine-tuning the metric head onto the existing Base/Small backbones, mirroring DA3METRIC-LARGE.
Even a single additional Small metric checkpoint would be valuable, and the Apache-2.0 license on
the current metric model makes a small variant especially useful for production/edge use.
Additional context
- The backbones already exist (
DA3-BASE, DA3-SMALL), so this is primarily a metric fine-tune
at smaller scale rather than new architecture.
- Happy to benchmark candidate checkpoints on Unity Inference Engine / mobile XR and report back
on accuracy and runtime if that's useful.
Is your feature request related to a problem?
The Monocular Metric series currently ships only at Large scale (
DA3METRIC-LARGE, 0.35B).The any-view Main series already provides
DA3-BASE(0.12B) andDA3-SMALL(0.08B), andthere's a
DA3MONO-LARGErelative model — but all of the smaller checkpoints are relative-only.There is no lightweight checkpoint that outputs real-world metric scale.
This is a blocker for real-time / on-device deployment. On edge runtimes — Unity Inference
Engine (Sentis), mobile, standalone XR headsets — the Small/Base models are the only ones that
run at interactive frame rates and are small enough to convert and quantize for the target,
but they're relative-only. The Large metric model is both too heavy for these targets and, in
practice, harder to get running in these runtimes. So any application needing absolute distance
on-device (AR/XR, robotics, assistive/accessibility tools) currently has to choose between
metric scale and a deployable footprint.
For context: Depth Anything V2 did release metric checkpoints at Small/Base/Large, so the move
to DA3 effectively removes the small-footprint metric option for edge users.
Describe the solution you'd like
Release metric checkpoints at the smaller scales —
DA3Metric-BaseandDA3Metric-Small— byfine-tuning the metric head onto the existing Base/Small backbones, mirroring
DA3METRIC-LARGE.Even a single additional Small metric checkpoint would be valuable, and the Apache-2.0 license on
the current metric model makes a small variant especially useful for production/edge use.
Additional context
DA3-BASE,DA3-SMALL), so this is primarily a metric fine-tuneat smaller scale rather than new architecture.
on accuracy and runtime if that's useful.