docs/API.md

📚 DepthAnything3 API Documentation

📑 Table of Contents

1. 📖 Overview
2. 💡 Usage Examples
3. 🔧 Core API
   • DepthAnything3 Class
   • inference() Method
4. ⚙️ Parameters
   • Input Parameters
   • Pose Alignment Parameters
   • Feature Export Parameters
   • Rendering Parameters
   • Processing Parameters
   • Export Parameters
5. 📤 Export Formats
6. ↩️ Return Value

📖 Overview

This documentation provides comprehensive API reference for DepthAnything3, including usage examples, parameter specifications, export formats, and advanced features. It covers both basic pose and depth estimation workflows and advanced pose-conditioned processing with multiple export capabilities.

💡 Usage Examples

Here are quick examples to get you started:

🚀 Basic Depth Estimation

from depth_anything_3.api import DepthAnything3

# Initialize and run inference
model = DepthAnything3.from_pretrained("depth-anything/DA3NESTED-GIANT-LARGE").to("cuda")
prediction = model.inference(["image1.jpg", "image2.jpg"])

📷 Pose-Conditioned Depth Estimation

import numpy as np

# With camera parameters for better consistency
prediction = model.inference(
    image=["image1.jpg", "image2.jpg"],
    extrinsics=extrinsics_array,  # (N, 4, 4)
    intrinsics=intrinsics_array   # (N, 3, 3)
)

📤 Export Results

# Export depth data and 3D visualization
prediction = model.inference(
    image=image_paths,
    export_dir="./output",
    export_format="mini_npz-glb"
)

🔍 Feature Extraction

# Export intermediate features from specific layers
prediction = model.inference(
    image=image_paths,
    export_dir="./output",
    export_format="feat_vis",
    export_feat_layers=[0, 1, 2]  # Export features from layers 0, 1, 2
)

✨ Advanced Export with Gaussian Splatting

# Export multiple formats including Gaussian Splatting
# Note: infer_gs=True requires da3-giant or da3nested-giant-large model
model = DepthAnything3(model_name="da3-giant").to("cuda")

prediction = model.inference(
    image=image_paths,
    extrinsics=extrinsics_array,
    intrinsics=intrinsics_array,
    export_dir="./output",
    export_format="npz-glb-gs_ply-gs_video",
    align_to_input_ext_scale=True,
    infer_gs=True,  # Required for gs_ply and gs_video exports
)

🎨 Advanced Export with Feature Visualization

# Export with intermediate feature visualization
prediction = model.inference(
    image=image_paths,
    export_dir="./output",
    export_format="mini_npz-glb-depth_vis-feat_vis",
    export_feat_layers=[0, 5, 10, 15, 20],
    feat_vis_fps=30,
)

📐 Using Ray-Based Pose Estimation

# Use ray-based pose estimation instead of camera decoder
prediction = model.inference(
    image=image_paths,
    export_dir="./output",
    export_format="glb",
    use_ray_pose=True,  # Enable ray-based pose estimation
)

🎯 Reference View Selection

# For multi-view inputs, automatically select the best reference view
prediction = model.inference(
    image=image_paths,
    ref_view_strategy="saddle_balanced",  # Default: balanced selection
)

# For video sequences, use middle frame as reference
prediction = model.inference(
    image=video_frames,
    ref_view_strategy="middle",  # Good for temporally ordered inputs
)

🔧 Core API

🔨 DepthAnything3 Class

The main API class that provides depth estimation capabilities with optional pose conditioning.

🎯 Initialization

from depth_anything_3 import DepthAnything3

# Initialize the model with a model name
model = DepthAnything3(model_name="da3-large")
model = model.to("cuda")  # Move to GPU

Parameters:

• model_name (str, default: "da3-large"): The name of the model preset to use.
  • Available models:
    • 🦾 "da3-giant" - 1.15B params, any-view model with GS support
    • ⭐ "da3-large" - 0.35B params, any-view model (recommended for most use cases)
    • 📦 "da3-base" - 0.12B params, any-view model
    • 🪶 "da3-small" - 0.08B params, any-view model
    • 👁️ "da3mono-large" - 0.35B params, monocular depth only
    • 📏 "da3metric-large" - 0.35B params, metric depth with sky segmentation
    • 🎯 "da3nested-giant-large" - 1.40B params, nested model with all features

🚀 inference() Method

The primary inference method that processes images and returns depth predictions.

prediction = model.inference(
    image=image_list,
    extrinsics=extrinsics_array,      # Optional
    intrinsics=intrinsics_array,      # Optional
    align_to_input_ext_scale=True,   # Whether to align predicted poses to input scale
    infer_gs=True,                   # Enable Gaussian branch for gs exports
    use_ray_pose=False,              # Use ray-based pose estimation instead of camera decoder
    ref_view_strategy="saddle_balanced",  # Reference view selection strategy
    render_exts=render_extrinsics,    # Optional renders for gs_video
    render_ixts=render_intrinsics,    # Optional renders for gs_video
    render_hw=(height, width),        # Optional renders for gs_video
    process_res=504,
    process_res_method="upper_bound_resize",
    export_dir="output_directory",    # Optional
    export_format="mini_npz",
    export_feat_layers=[],            # List of layer indices to export features from
    conf_thresh_percentile=40.0,      # Confidence threshold percentile for depth map in GLB export
    num_max_points=1_000_000,         # Maximum number of points to export in GLB export
    show_cameras=True,                # Whether to show cameras in GLB export
    feat_vis_fps=15,                  # Frames per second for feature visualization in feat_vis export
    export_kwargs={}                  # Optional, additional arguments to export functions. export_format:key:val, see 'Parameters/Export Parameters' for details
)

⚙️ Parameters

📸 Input Parameters

image (required)

• Type: List[Union[np.ndarray, Image.Image, str]]
• Description: List of input images. Can be numpy arrays, PIL Images, or file paths.
• Example:

  # From file paths
  image = ["image1.jpg", "image2.jpg", "image3.jpg"]
  
  # From numpy arrays
  image = [np.array(img1), np.array(img2)]
  
  # From PIL Images
  image = [Image.open("image1.jpg"), Image.open("image2.jpg")]
  

extrinsics (optional)

• Type: Optional[np.ndarray]
• Shape: (N, 4, 4) where N is the number of input images
• Description: Camera extrinsic matrices (world-to-camera transformation). When provided, enables pose-conditioned depth estimation mode.
• Note: If not provided, the model operates in standard depth estimation mode.

intrinsics (optional)

• Type: Optional[np.ndarray]
• Shape: (N, 3, 3) where N is the number of input images
• Description: Camera intrinsic matrices containing focal length and principal point information. When provided, enables pose-conditioned depth estimation mode.

🎯 Pose Alignment Parameters

align_to_input_ext_scale (default: True)

• Type: bool
• Description: When True the predicted extrinsics are replaced with the input ones and the depth maps are rescaled to match their metric scale. When False the function returns the internally aligned poses computed via Umeyama alignment.

infer_gs (default: False)

• Type: bool
• Description: Enable Gaussian Splatting branch for gaussian splatting exports. Required when using gs_ply or gs_video export formats.

use_ray_pose (default: False)

• Type: bool
• Description: Use ray-based pose estimation instead of camera decoder for pose prediction. When True, the model uses ray prediction heads to estimate camera poses; when False, it uses the camera decoder approach.

ref_view_strategy (default: "saddle_balanced")

• Type: str
• Description: Strategy for selecting the reference view from multiple input views. Options: "first", "middle", "saddle_balanced", "saddle_sim_range". Only applied when number of views ≥ 3. See detailed documentation for strategy comparisons.
• Available strategies:
  • "saddle_balanced": Selects view with balanced features across multiple metrics (recommended default)
  • "saddle_sim_range": Selects view with largest similarity range
  • "first": Always uses first view (not recommended, equivalent to no reordering for views < 3)
  • "middle": Uses middle view (recommended for video sequences)

🔍 Feature Export Parameters

export_feat_layers (default: [])

• Type: List[int]
• Description: List of layer indices to export intermediate features from. Features are stored in the aux dictionary of the Prediction object with keys like feat_layer_0, feat_layer_1, etc.

🎥 Rendering Parameters

These arguments are only used when exporting Gaussian-splatting videos (include "gs_video" in export_format). They describe an auxiliary camera trajectory with M views.

render_exts (optional)

• Type: Optional[np.ndarray]
• Shape: (M, 4, 4)
• Description: Camera extrinsics for the synthesized trajectory. If omitted, the exporter falls back to the predicted poses.

render_ixts (optional)

• Type: Optional[np.ndarray]
• Shape: (M, 3, 3)
• Description: Camera intrinsics for each rendered frame. Leave None to reuse the input intrinsics.

render_hw (optional)

• Type: Optional[Tuple[int, int]]
• Description: Explicit output resolution (height, width) for the rendered frames. Defaults to the input resolution when not provided.

⚡ Processing Parameters

process_res (default: 504)

• Type: int
• Description: Base resolution for processing. The model will resize images to this resolution for inference.

process_res_method (default: "upper_bound_resize")

• Type: str
• Description: Method for resizing images to the target resolution.
• Options:
  • "upper_bound_resize": Resize so that the specified dimension (504) becomes the longer side
  • "lower_bound_resize": Resize so that the specified dimension (504) becomes the shorter side
• Example:
  • Input: 1200×1600 → Output: 378×504 (with process_res=504, process_res_method="upper_bound_resize")
  • Input: 504×672 → Output: 504×672 (no change needed)

📦 Export Parameters

export_dir (optional)

• Type: Optional[str]
• Description: Directory path where exported files will be saved. If not provided, no files will be exported.

export_format (default: "mini_npz")

• Type: str
• Description: Format for exporting results. Supports multiple formats separated by -.
• Example: "mini_npz-glb" exports both mini_npz and glb formats.

🌐 GLB Export Parameters

These parameters are passed directly to the inference() method and only apply when export_format includes "glb".

conf_thresh_percentile (default: 40.0)

• Type: float
• Description: Lower percentile for adaptive confidence threshold. Points below this confidence percentile will be filtered out from the point cloud.

num_max_points (default: 1,000,000)

• Type: int
• Description: Maximum number of points in the exported point cloud. If the point cloud exceeds this limit, it will be downsampled.

show_cameras (default: True)

• Type: bool
• Description: Whether to include camera wireframes in the exported GLB file for visualization.

🎨 Feature Visualization Parameters

These parameters are passed directly to the inference() method and only apply when export_format includes "feat_vis".

feat_vis_fps (default: 15)

• Type: int
• Description: Frame rate for the output video when visualizing features across multiple images.

✨🎥 3DGS and 3DGS Video Parameters

These parameters are passed directly to the inference() method and only apply when export_format includes "gs_ply" or "gs_video".

export_kwargs (default: {})

• Type: dict[str, dict[str, Any]]
• Description: Per-format extra arguments passed to export functions, mainly for "gs_ply" and "gs_video".
  • Access pattern: export_kwargs[export_format][key] = value
  • Example:

    {
        "gs_ply": {
            "gs_views_interval": 1,
        },
        "gs_video": {
            "trj_mode": "interpolate_smooth",
            "chunk_size": 1,
            "vis_depth": None,
        },
    }
    

📤 Export Formats

The API supports multiple export formats for different use cases:

📊 mini_npz

• Description: Minimal NPZ format containing essential data
• Contents: depth, conf, exts, ixts
• Use case: Lightweight storage for depth data with camera parameters

📦 npz

• Description: Full NPZ format with comprehensive data
• Contents: depth, conf, exts, ixts, image, etc.
• Use case: Complete data export for advanced processing

🌐 glb

• Description: 3D visualization format with point cloud and camera poses
• Contents:
  • Point cloud with colors from original images
  • Camera wireframes for visualization
  • Confidence-based filtering and downsampling
• Use case: 3D visualization, inspection, and analysis
• Features:
  • Automatic sky depth handling
  • Confidence threshold filtering
  • Background filtering (black/white)
  • Scene scale normalization
• Parameters (passed via inference() method directly):
  • conf_thresh_percentile (float, default: 40.0): Lower percentile for adaptive confidence threshold. Points below this confidence percentile will be filtered out.
  • num_max_points (int, default: 1,000,000): Maximum number of points in the exported point cloud. If exceeded, points will be downsampled.
  • show_cameras (bool, default: True): Whether to include camera wireframes in the exported GLB file for visualization.

✨ gs_ply

• Description: Gaussian Splatting point cloud format
• Contents: 3DGS data in PLY format. Compatible with standard 3DGS viewers such as SuperSplat (recommended), SPARK.
• Use case: Gaussian Splatting reconstruction
• Requirements: Must set infer_gs=True when calling inference(). Only supported by da3-giant and da3nested-giant-large models.
• Additional configs, provided via export_kwargs (see Export Parameters):
  • gs_views_interval: Export to 3DGS every N views, default: 1.

🎥 gs_video

• Description: Rasterized 3DGS to obtain videos
• Contents: A video of 3DGS-rasterized views using either provided viewpoints or a predefined camera trajectory.
• Use case: Video rendering for Gaussian Splatting
• Requirements: Must set infer_gs=True when calling inference(). Only supported by da3-giant and da3nested-giant-large models.
• Note: Can optionally use render_exts, render_ixts, and render_hw parameters in inference() method to specify novel viewpoints.
• Additional configs, provided via export_kwargs (see Export Parameters):
  • extrinsics: Optional world-to-camera poses for novel views. Falls back to the predicted poses of input views if not provided. (Alternatively, use render_exts parameter in inference())
  • intrinsics: Optional camera intrinsics for novel views. Falls back to the predicted intrinsics of input views if not provided. (Alternatively, use render_ixts parameter in inference())
  • out_image_hw: Optional output resolution H x W. Falls back to input resolution if not provided. (Alternatively, use render_hw parameter in inference())
  • chunk_size: Number of views rasterized per batch. Default: 8.
  • trj_mode: Predefined camera trajectory for novel-view rendering.
  • color_mode: Same as render_mode in gsplat.
  • vis_depth: How depth is combined with RGB. Default: hcat (horizontal concatenation).
  • enable_tqdm: Whether to display a tqdm progress bar during rendering.
  • output_name: File name of the rendered video.
  • video_quality: Video quality to save. Default: high.
    • high: High quality video (default)
    • medium: Medium quality video (balance of storage space and quality)
    • low: Low quality video (fewer storage space)

🔍 feat_vis

• Description: Feature visualization format
• Contents: PCA-visualized intermediate features from specified layers
• Use case: Model interpretability and feature analysis
• Note: Requires export_feat_layers to be specified
• Parameters (passed via inference() method directly):
  • feat_vis_fps (int, default: 15): Frame rate for the output video when visualizing features across multiple images.

🎨 depth_vis

• Description: Depth visualization format
• Contents: Color-coded depth maps alongside original images
• Use case: Visual inspection of depth estimation quality

🔗 Multiple Format Export

You can export multiple formats simultaneously by separating them with -:

# Export both mini_npz and glb formats
export_format = "mini_npz-glb"

# Export multiple formats
export_format = "npz-glb-gs_ply"

↩️ Return Value

The inference() method returns a Prediction object with the following attributes:

📊 Core Outputs

• depth: np.ndarray - Estimated depth maps with shape (N, H, W) where N is the number of images, H is height, and W is width.
• conf: np.ndarray - Confidence maps with shape (N, H, W) indicating prediction reliability (optional, depends on model).

📷 Camera Parameters

• extrinsics: np.ndarray - Camera extrinsic matrices with shape (N, 3, 4) representing world-to-camera transformations. Only present if camera poses were estimated or provided as input.
• intrinsics: np.ndarray - Camera intrinsic matrices with shape (N, 3, 3) containing focal length and principal point information. Only present if poses were estimated or provided as input.

🎁 Additional Outputs

• processed_images: np.ndarray - Preprocessed input images with shape (N, H, W, 3) in RGB format (0-255 uint8).
• aux: dict - Auxiliary outputs including:
  • feat_layer_X: Intermediate features from layer X (if export_feat_layers was specified)
  • gaussians: 3D Gaussian Splats data (if infer_gs=True)

💻 Usage Example

prediction = model.inference(image=["img1.jpg", "img2.jpg"])

# Access depth maps
depth_maps = prediction.depth  # shape: (2, H, W)

# Access confidence
if hasattr(prediction, 'conf'):
    confidence = prediction.conf

# Access camera parameters (if available)
if hasattr(prediction, 'extrinsics'):
    camera_poses = prediction.extrinsics  # shape: (2, 4, 4)

if hasattr(prediction, 'intrinsics'):
    camera_intrinsics = prediction.intrinsics  # shape: (2, 3, 3)

# Access intermediate features (if export_feat_layers was set)
if hasattr(prediction, 'aux') and 'feat_layer_0' in prediction.aux:
    features = prediction.aux['feat_layer_0']