DA3-SMALL

by depth-anything

Lightweight monocular and multi-view depth estimation with unified depth-ray representation

161Kdl/month

24Mparams

HuggingFace Use in Pipeline

Identifiers

Model ID

depth-anything/DA3-SMALL

Feature URI

mixpeek://image_extractor@v1/depth_anything_v3_small_v1

Overview

Depth Anything 3 Small (DA3-Small) is the compact variant of ByteDance's Depth Anything 3 family, which uses a single plain Vision Transformer with a unified depth-ray representation to handle monocular depth estimation, multi-view depth estimation, stereo matching, and camera pose estimation from any number of input views.

Unlike Depth Anything 2 which only handles single images, DA3 processes single images, stereo pairs, multi-view collections, and videos with geometrically consistent outputs. The Small variant uses a DINOv2 ViT-Small backbone, providing fast inference suitable for real-time applications and edge deployment. On Mixpeek, DA3-Small extracts depth maps from video frames and images, enabling spatial understanding, 3D-aware content filtering, and depth-based scene segmentation in retrieval pipelines.

Architecture

DINOv2 ViT-Small backbone with unified depth-ray prediction head. Single plain transformer processes any number of input views. Depth-ray representation eliminates need for multi-task learning. Supports monocular, stereo, and multi-view depth estimation in a single model.

Mixpeek SDK Integration

from mixpeek import Mixpeek
mx = Mixpeek(api_key="YOUR_KEY")

mx.ingest(
    collection_id="video-library",
    source="s3://footage/",
    extractors=[
        {
            "type": "scene_caption",
            "model": "depth-anything/DA3-SMALL",
            "output_feature": "depth_map"
        }
    ]
)

Capabilities

Monocular, stereo, and multi-view depth estimation
Camera pose estimation from arbitrary view sets
Unified depth-ray representation for geometric consistency
Lightweight ViT-Small backbone for fast inference
44.3% better camera pose accuracy than prior SOTA (VGGT)

Use Cases on Mixpeek

Spatial content filtering: retrieve scenes by depth characteristics (close-up vs. wide shot)

3D-aware video analysis: extract depth maps for scene understanding in video pipelines

Augmented reality content indexing: tag content with spatial depth metadata for AR applications

Benchmarks

Dataset	Metric	Score	Source
DA3 family vs VGGT (camera pose)	Accuracy improvement	+44.3% avg	ByteDance, 2025 — arxiv:2511.10647
DA3 family vs DA2 (monocular)	Geometric accuracy	+25.1% avg	ByteDance, 2025 — arxiv:2511.10647