You can use the multi-view node to use the cross attention feature of the main class of models. This is done to have a more consistent depth across frames of a video.

https://github.com/user-attachments/assets/0ee6eab5-1640-4689-9545-20a4541a0aba

You can reconstruct 3D point clouds!

https://github.com/user-attachments/assets/0ff44819-4995-4393-aec3-f3b242e1a916

Even from multiple views, with the option to either match them (with icp) or leave them to use the predicted camera positions.

https://github.com/user-attachments/assets/fb490fc8-f0c8-48ec-8615-e44f5c48a3c4

Description

Depth Anything V3 is the latest depth estimation model that predicts spatially consistent geometry from visual inputs.

Published: November 14, 2025 Paper: Depth Anything 3: Recovering the Visual Space from Any Views

Nodes

Model Loading

Download And Load Depth Anything V3 Model - Downloads and loads model from HuggingFace

Inputs: model (variant), precision (auto/fp16/fp32/bf16)
Output: da3_model
Models auto-download to ComfyUI/models/depthanything3/

DA3 Enable Tiled Processing - Configure model for high-resolution images

Inputs: da3_model, tile_size, overlap
Output: da3_model (with tiled config)
Use for 4K+ images to avoid OOM errors

Inference Nodes

Depth Anything V3 - Unified depth estimation with multiple normalization modes

Inputs: da3_model, images, normalization_mode, camera_params (optional), resize_method, invert_depth, keep_model_size
Outputs: depth, confidence, ray_origin, ray_direction, extrinsics, intrinsics, sky_mask
Normalization Modes:
- Standard: Original V3 min-max normalization (0-1 range, includes sky in normalization)
- V2-Style (default): Disparity-based with content-aware contrast - best for ControlNet workflows
  - Sky appears BLACK (like Depth Anything V2)
  - Content-only normalization excludes sky from range calculation
  - Percentile-based contrast enhancement (1st-99th percentile)
  - Enhanced depth gradations via contrast boost
  - Subtle edge anti-aliasing for natural transitions
  - Contribution by Ltamann (TBG)
- Raw: No normalization, outputs metric depth values for 3D reconstruction/point clouds

Note: The unified node always outputs all available data. Connect only the outputs you need - unused outputs are simply ignored.

Depth Anything V3 (Multi-View) - Process multiple images with cross-view attention

Inputs: da3_model, images (batch), normalization_mode, resize_method, invert_depth
Outputs: depth, confidence, ray_origin, ray_direction, extrinsics, intrinsics, sky_mask
Normalization modes: Standard, V2-Style (default), Raw
Use for video frames or multiple angles of same scene
Normalizes depth across all views together for temporal/spatial consistency

3D Processing

DA3 to Point Cloud - Convert depth to point cloud using geometric unprojection

Inputs: depth_raw, confidence, sky_mask (optional but recommended), intrinsics (optional), source_image (optional)
Output: pointcloud
Sky filtering: When sky_mask is connected, sky pixels are automatically excluded from the point cloud
Input validation: Automatically detects and rejects normalized depth inputs (requires Raw mode)

DA3 to Point Cloud (Multi view) - Convert depth to point cloud using geometric unprojection

DA3 Save Point Cloud - Export to PLY format

DA3 to 3D Gaussians - Extract 3D Gaussian splats (placeholder)

DA3 Save 3D Gaussians - Export Gaussians to PLY

Camera Utilities

DA3 Create Camera Parameters - Create camera conditioning input

DA3 Parse Camera Pose - Parse camera parameters from JSON

Model Variants

Model	Size	Features
DA3-Small	80M	Fast, good quality
DA3-Base	220M	Balanced quality and speed
DA3-Large	350M	High quality, balanced
DA3-Giant	1.15B	Best quality, slower
DA3Mono-Large	350M	Optimized for monocular depth
DA3Metric-Large	350M	Metric depth estimation
DA3Nested-Giant-Large	1.4B	Combined model with metric scaling

Model Capabilities

Different models support different features:

Feature	Small/Base/Large/Giant	Mono-Large	Metric-Large	Nested
Sky Segmentation	❌	✅	✅	✅
Camera Conditioning	✅	❌	❌	✅
Multi-View Attention	✅	⚠️	⚠️	✅
3D Gaussians	✅*	❌	❌	✅*
Ray Maps	✅	❌	❌	✅

✅ = Fully supported
❌ = Not available (returns zeros/ignored)
⚠️ = Works but no cross-view attention benefit (images processed independently)
✅* = Requires fine-tuned model weights (placeholder in current release)

Choose your model based on needs:

Need sky masks? → Use Mono/Metric/Nested (required for V2-Style normalization)
Need camera conditioning? → Use Main series or Nested
Processing video/multi-view? → Use Main series or Nested for consistency
Single images only? → Any model works

Workflow Tips

For ControlNet Depth Workflows

Use Mono or Metric models (they provide sky segmentation)
Set normalization_mode to V2-Style (default)
Connect the depth output to your ControlNet node
Enjoy clean depth maps with proper sky handling!

For 3D Reconstruction (Point Clouds)

Use any model (Mono/Metric recommended for sky filtering)
Set normalization_mode to Raw
Connect depth → depth_raw, confidence → confidence, sky_mask → sky_mask to DA3 to Point Cloud
Sky pixels will be automatically excluded if sky_mask is connected
Important: Point cloud nodes validate input and will raise an error if normalized depth is detected (prevents incorrect 3D output)

Credits

Original Paper: Haotong Lin, Sili Chen, Jun Hao Liew, et al. (ByteDance Seed Team)
Original Implementation: PozzettiAndrea
V2-Style Normalization: Ltamann (TBG) - See TBG Takeaways: Depth Anything V3 for workflow examples
Based on: Official Depth Anything 3 repository
Inspiration: kijai's ComfyUI-Depth-Anything-V2

License

Model architecture files based on Depth Anything 3 (Apache 2.0 / CC BY-NC 4.0 depending on model).

Note: Some models (Giant, Nested) use CC BY-NC 4.0 license (non-commercial use only).