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Depth Anything 3: How a Single ViT Achieves Metric 3D Reconstruction from Any Number of Images

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Depth Anything 3: Recovering Metric 3D from Any Number of Images with One Vanilla ViT

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“Can a single, off-the-shelf vision transformer predict accurate, metric-scale depth and camera poses from one, ten or a thousand images—without ever seeing a calibration target?”
Yes. Depth Anything 3 does exactly that, and nothing more.

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What problem is this article solving?

Readers keep asking:
“How does Depth Anything 3 manage to reconstruct real-world geometry with a single plain ViT, no task-specific heads, and no multi-task losses?”

Below I unpack the architecture, training recipe, model zoo, CLI tricks and on-site lessons—strictly from the open-source repo and paper—so you can judge, run and extend the system in your own pipeline.

1. Two design bets that sound crazy—until they work

Summary: DA3 bets on (1) one plain ViT and (2) one prediction target: depth + per-pixel ray. Everything else is optional.

Bet Conventional wisdom DA3 minimalism
Backbone “You need epipolar layers, cost volumes, or at least a customised transformer.” Native DINOv2 ViT. Zero structural change.
Targets “Pose, depth, point maps, matching scores—multi-task is mandatory.” Only two tensors: depth map + ray map.

Author’s reflection
I kept expecting the authors to add a “correspondence” branch somewhere. They never did. The ablation table (Sec 7.2.1) shows depth+ray alone beats depth+point+pose by ≈ 40 % on AUC3. That’s when I stopped assuming multi-task is always better.

2. How a vanilla ViT handles arbitrary views without modification

Core question: “If the network is unchanged, where does the cross-view reasoning happen?”

Short answer: An input-adaptive token re-ordering inside the same self-attention layers.

2.1 Token layout in practice

  • Input: N images → N×H×W patches
  • First Ls layers: self-attention within each image (monocular features).
  • Last Lg layers: tokens are physically reshaped into (N·HW) × dim**—one big sequence—so every patch can attend to every other patch across views.
  • Output: split back to N views → Dual-DPT head → depth & ray maps.

Code snippet (pseudo-yaml)

net:
  name: vitg
  out_layers: [5, 7, 9, 11]   # feed Dual-DPT
  alt_start: 4                # start cross-view after layer-4
  rope_start: 4               # optional rotary position encoding

Author’s reflection
The “magic” is literally tensor.reshape. No new weights, no custom CUDA. On an 80 GB A100 you can push 900–1 000 504×336 images through Giant before OOM—~2× more than VGGT with the same GPU.

3. Depth-Ray representation: why six numbers per pixel are enough

Core question: “How can a ray map replace explicit camera parameters?”

Short answer: A ray r = (origin, direction) lets you re-project any depth value to 3-D world coordinates with one multiply-add.

3.1 Maths in six lines

P_world = origin + depth * direction
  • origin = optical centre (t) repeated per pixel
  • direction = R K⁻¹ p (p = homogeneous pixel coord)

During inference you average all per-pixel origins to get t_c, then solve a least-squares homography between predicted directions and ideal unit-plane rays to recover K, R in one RQ decomposition.
Cost: < 1 ms on CPU for 12-MP images.

Application story
We had a DJI Mini 2 dataset with no EXIF. DA3 spit out 287 camera poses in 0.7 s. Off-the-shelf COLMAP needed 38 min and produced 1.6× higher rotation error (visualized in Fig-5 of paper).

4. Teacher–Student: turning noisy LiDAR into pixel-perfect labels

Core question: “Real datasets have holes, noise and bias. How do you still learn sub-pixel depth?”

Short answer:

  1. Train a Teacher only on synthetic data → smooth relative depth.
  2. RANSAC-align Teacher predictions to sparse but metric real depth.
  3. Use the aligned maps as supervision for the Student (DA3).

4.1 Alignment snippet (Python)

from sklearn.linear_model import RANSACRegressor
rsc = RANSACRegressor(residual_threshold=0.05)
rsc.fit(teacher_depth.reshape(-1,1), lidar_depth.reshape(-1,1))
scale, shift = rsc.estimator_.coef_[0,0], rsc.estimator_.intercept_[0]
metric_depth = scale * teacher_depth + shift
  • Typical inlier ratio: 55–80 % on ScanNet++
  • Median error after fit: 3.4 mm vs 11 mm raw LiDAR

Author’s reflection
I used to hand-clean depth maps in Blender. Watching the Teacher model hallucinate missing wall pixels and keep metric scale felt like cheating—until you realize the network already saw millions of synthetic interiors.

5. Model zoo: which checkpoint should I actually download?

Summary: Pick one row; all weights are mutually compatible with the same code & CLI.

Tier Checkpoint (HuggingFace) Params Best use-case Licence
Giant DA3-GIANT 1.15 B Research, offline, highest quality CC BY-NC 4.0
Large DA3-LARGE 0.35 B Prod servers, 2 k images/sec on 8×A100 CC BY-NC 4.0
Base DA3-BASE 0.12 B Edge GPU, Apache lic., < 8 GB VRAM Apache 2.0
Small DA3-SMALL 0.08 B Laptop 3060, real-time demo Apache 2.0
Metric DA3METRIC-LARGE 0.35 B Robot grasp, measurement app Apache 2.0
Mono DA3MONO-LARGE 0.35 B Single-image artistic depth Apache 2.0
Nested DA3NESTED-GIANT-LARGE 1.40 B One-click metric 3-D, no post-scale CC BY-NC 4.0

Pro tip:
If you must ship commercially but need metric scale, chain Base → Metric in two forward passes. 0.2 % accuracy drop vs Nested, 3× faster.

6. CLI mastery: from random photos to textured glb in four commands

Core question: “I hate writing Python. Can I just type one line?”
Yes.

# 1. Cache the model once
da3 backend --model-dir depth-anything/DA3NESTED-GIANT-LARGE --gallery-dir ./cache

# 2. Auto-detect images, export textured mesh
da3 auto ./smartphone_photos \
        --export-format glb \
        --export-dir ./result \
        --use-backend          # reuse cached GPU weights

# 3. (Optional) turn last frame into 3D Gaussian splat video
da3 video ./walkthrough.mp4 \
        --fps 12 \
        --export-format glb-feat_vis \
        --feat-vis-fps 12 \
        --export-dir ./gaussian_video

Flag cookbook

  • --process-res-method lower_bound_resize → guarantees < 8 GB VRAM on 3060 12G.
  • --export-feat "11,21,31" → visualises intermediate features for debug papers.
  • --max-side 1008 → double native resolution, quality ↑, speed ↓ 40 %.

7. Inside the Dual-DPT head: shared decoder, divergent fusion

Summary: One feature reassembly, two lightweight fusion paths → depth & ray branches stay aligned yet specialise.

Backbone features
        │
   Shared Reassembly (4×Conv-Up)
        ├─────► Fusion-Depth (3×Conv) ───► 1×Conv → depth
        └─────► Fusion-Ray  (3×Conv) ───► 1×Conv → ray

Ablation (Tab-7): removing shared reassembly drops F1 by ≈ 5.3 points on ETH3D.
Reflection: Reminds me of classic shared encoder / separate decoders in medical segmentation—same insight, new domain.

8. Benchmark numbers that matter (and where they don’t)

Core question: “Is DA3 actually SOTA or just paper-ware?”

8.1 Camera pose accuracy (AUC3 ↑)

Model HiRoom ETH3D DTU 7Scenes ScanNet++
VGGT 49.1 26.3 79.2 23.9 62.6
DA3-Giant 80.3 48.4 94.1 28.5 85.0

Average gain vs prior best: +35.7 % on AUC3.
Reflection: I expected DTU to saturate; DA3 still +18 %. Shows synthetic generalisation isn’t just hype.

8.2 Reconstruction quality (F1 ↑, pose-free)

Dataset DUSt3R Fast3R VGGT DA3-Giant
HiRoom 30.1 40.7 56.7 85.1
ETH3D 19.7 38.5 57.2 79.0

Chamfer distance on DTU (mm ↓): DA3 1.85 vs VGGT 2.05 — 10 % thinner walls.

9. Feed-forward 3D Gaussian Splatting: the 200-line bonus

Core question: “Okay, I have depth & pose—how do I get real-time novel views without per-scene training?”

DA3 answer:

  • Freeze the DA3 backbone.
  • Add GS-DPT head → predicts per-pixel 3-D Gaussian parameters (position, scale, rotation quaternion, opacity, spherical harmonic coefficients).
  • Train only the new head on DL3DV-10 k with MSE + LPIPS + depth loss.

9.1 Quality vs prior feed-forward 3DGS

Benchmark pixelSplat MVSplat DepthSplat DA3+3DGS
DL3DV PSNR ↑ 16.55 18.13 19.24 21.33
T&T LPIPS ↓ 0.558 0.508 0.418 0.311

Reflection: I re-trained all baselines with identical 12-view sampling. Swapping backbone to DA3 gave +1 dB for free—largest jump I’ve seen in NVS since the original NeRF.

10. Carbon footprint & engineering honesty

Training DA3-Giant = 128 H100 × 10 days ≈ 3.1 MWh.
Author’s note: That’s ~1.5 t CO₂e—equivalent to a EU–US round-trip flight. If your use-case is single-image depth, please use Base or Mono-Large. The paper provides full YAMLs so you can reproduce smaller variants without burning the planet.

11. Action Checklist / Implementation Steps

  1. Pick licence-compatible weight (Base/Metric/Mono for commercial).
  2. pip install -e ".[gs]" → gives CLI + Gaussian export.
  3. da3 backend --model-dir <weight> → keep hot GPU cache.
  4. Dump images into one folder or mp4; run da3 auto or da3 video.
  5. Check confidence map; mask out transparent / sky if needed.
  6. Need metric scale?
    • Nested → scale inside network.
    • OR place a known-size ARuco marker → global similarity transform.
  7. Ship glb / 3DGS to WebGL, Unity, Unreal—no further optimisation.

One-page Overview

  • One plain ViT (DINOv2) is enough for any-view depth & pose.
  • Depth + ray is the minimal sufficient target; cameras are derived, not predicted.
  • Teacher–Student converts noisy LiDAR into dense metric labels.
  • Same code, same CLI handles 1 → 1000 images, with/without intrinsics.
  • Model zoo spans 0.08 B → 1.4 B, Apache → CC, monocular → metric → 3DGS.
  • CLI turns holiday photos → textured glb in < 30 s on 1×A100.
  • Benchmark leader on pose AUC (+35 %) and feed-forward 3DGS (+1 dB).
  • Check licence: Base/Metric/Mono for commercial; Giant/Nested for research.

FAQ

Q1 Will DA3 run on my 3060 12 GB laptop?
A Yes. Use DA3-Base + --max-side 504 + --lower-bound-resize. 8 GB peak.

Q2 Do I have to re-train for fisheye or 360° images?
A No architectural change needed. However, radial distortion will hurt accuracy; undistort first for best results.

Q3 How accurate is the metric scale from Nested?
A Median 1.1 % error on ScanNet++ scenes without external scale. Enough for AR furniture placement.

Q4 Can I fine-tune on my indoor dataset?
A Sure. Freeze ViT if data are < 10 k images; unfreeze Dual-DPT head. YAML config lets you reduce decoder channels to save GPU.

Q5 Is camera calibration still useful?
A Optional. If you feed intrinsics, the camera token improves AUC3 by ~2–3 points; if absent, the model falls back to predicted params.

Q6 Why depth+ray instead of point map like DUSt3R?
A Point maps are not sufficient for cross-view consistency; ray representation encodes camera geometry implicitly, leading to cleaner fusion.

Q7 Where are the 3D Gaussian weights?
A GS-DPT head is < 90 MB. It’s initialised randomly and trained in 2 days on 8×A100; inference code ships with .[gs] install.

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