BLASt3R — depth model
Multi-channel depth model for BLASt3R, a method for bundle-adjusted 3D reconstruction and visual SLAM from image collections and video. Code: github.com/naver/blast3r.
This is one of the two checkpoints BLASt3R needs. The other is the matching
model, naver/blast3r-matcher.
Training of this checkpoint was initialized from DINOv3 pretrained weights, so these weights are a derivative work of DINOv3 — see License below.
Contents
config.json model class and its arguments
model.safetensors the weights
config.json names the class and hyperparameters, so loading a checkpoint never
unpickles anything.
Usage
from huggingface_hub import snapshot_download
ckpt_match = snapshot_download('naver/blast3r-matcher')
ckpt_depth = snapshot_download('naver/blast3r-depth')
Then run reconstruction from the BLASt3R code repository:
python scripts/run_offline.py \
--images /path/to/images \
--output /path/to/output \
--ckpt_match "$CKPT_MATCH" \
--ckpt_depth "$CKPT_DEPTH" \
--mono_cam
License
Released under the NAVER non-commercial license (LICENSE), which covers the
code and these checkpoints. Third-party subcomponents, the datasets these
weights were trained on, and the pretrained weights they were initialized from
are acknowledged in NOTICE; dataset terms pass through to the weights.
This checkpoint was initialized from
DINOv3 weights, made available by
Meta Platforms, Inc. under the
DINOv3 License.
Its use is therefore additionally subject to that license, including its
restrictions on military, warfare, nuclear, espionage and weapons-related use
and on activities subject to Trade Controls. As Section 1.b of that license
requires, a copy is distributed here as DINOv3_LICENSE.md.