Depth Anything

Depth Anything is a cutting-edge foundation model for monocular depth estimation, trained on 1.5 million diverse labeled images and 62 million unlabeled images. It achieves remarkable zero-shot performance across indoor, outdoor, and challenging scenarios without domain-specific fine-tuning. Built on a Vision Transformer backbone with dense prediction head, it represents the state-of-the-art in single-image depth prediction with strong robustness to varied lighting, weather, and scene types.

When to Use Depth Anything

Depth Anything is ideal for:

• Zero-shot depth prediction on new images without training
• 3D reconstruction from single photographs or video frames
• AR/VR applications requiring real-time scene depth understanding
• Autonomous systems needing robust depth estimation across conditions
• Photography effects like bokeh simulation or depth-based compositing
• Scene analysis for robotics, navigation, or spatial understanding
• General-purpose depth as a reliable default choice across domains

This is the go-to depth estimation model when you need accurate, robust depth maps without domain-specific training data.

Strengths

• Exceptional zero-shot performance: Works across indoor/outdoor/challenging scenes
• Strong generalization: Trained on 1.5M+ diverse images with 62M unlabeled data
• Multiple model sizes: Small, Base, Large variants for speed/accuracy tradeoff
• Robust to conditions: Handles varied lighting, weather, and image quality
• High detail preservation: Captures fine depth structure and object boundaries
• Consistent predictions: Stable depth across similar scenes
• Real-world applicability: Proven performance on real-world deployment scenarios
• Fine-tuning capable: Can be customized for specific domains if needed

Weaknesses

• Scale ambiguity: Predicts relative depth, not absolute metric depth
• Reflective surfaces: Struggles with mirrors, glass, water
• Transparent objects: Cannot accurately predict depth through transparency
• Textureless regions: May show noise in uniform areas
• Computational cost: Large model requires GPU for real-time
• No training by default: Pre-trained only, fine-tuning requires depth ground truth
• Inference only focus: Primarily designed for inference rather than training
• Memory requirements: High-resolution inputs need significant VRAM

Architecture Overview

Vision Transformer-based Design

Depth Anything uses a hierarchical ViT encoder with dense prediction decoder:

1. Image Encoder (ViT Backbone)

   • Splits image into patches
   • Processes through transformer layers
   • Extracts multi-scale features
   • Handles global context effectively

2. Dense Prediction Head (DPT-style)

   • Fuses multi-scale features
   • Progressive upsampling
   • Refines depth predictions at each scale
   • Outputs per-pixel depth map

3. Training Strategy

   • Supervised learning on labeled depth data (1.5M images)
   • Self-supervised learning on unlabeled images (62M images)
   • Auxiliary semantic learning task
   • Multi-dataset training for generalization

Model Variants:

Small

• Encoder: DINOv2 ViT-Small
• Parameters: ~24M
• Speed: ~30-50ms per image (GPU)
• Best for: Real-time applications

Base

• Encoder: DINOv2 ViT-Base
• Parameters: ~97M
• Speed: ~50-100ms per image (GPU)
• Best for: Balanced accuracy and speed

Large

• Encoder: DINOv2 ViT-Large
• Parameters: ~335M
• Speed: ~150-300ms per image (GPU)
• Best for: Maximum accuracy

Parameters

Inference Configuration

Finetuned Checkpoint (Optional)

• Type: Artifact
• Description: Custom fine-tuned model checkpoint
• Required: No (uses pre-trained Depth Anything V2 Large by default)
• Use case: When fine-tuned on domain-specific depth data
• Format: PyTorch .pth checkpoint

Input Image (Required)

• Type: ImageBlob (PNG format)
• Description: RGB image for depth estimation
• Required: Yes
• Resolution: Any (will be processed appropriately)
• Recommended: 384x384 to 1024x1024 for best quality
• Format: PNG (other formats supported via conversion)
• Channels: 3-channel RGB

Model Size (Default: "large")

• Options: ["small", "base", "large"]
• Type: String
• Description: Depth Anything model variant
• Recommendation:
  • "small" for real-time applications (30+ FPS on GPU)
  • "base" for balanced use cases (good accuracy, reasonable speed)
  • "large" for maximum accuracy (best quality, slower)
• Impact: Larger models more accurate but slower and memory-intensive

Output Type (Default: "depth")

• Options: ["depth", "disparity"]
• Type: String
• Description: Format of depth prediction output
• Recommendation:
  • "depth" for most applications (intuitive distance values)
  • "disparity" for stereo vision compatibility (inverse depth)
• Impact: Depth more intuitive, disparity better for certain algorithms

Output Format

Depth Map

• Type: BinaryBlob (NumPy array, .npy format)
• Description: Dense depth prediction for every pixel
• Shape: (H, W) matching input image resolution
• Values: Normalized depth (often 0-1 range) or metric if calibrated
• Usage: Primary output for depth-based applications

Depth Image

• Type: ImageBlob (PNG format)
• Description: Visualization of depth map as grayscale image
• Values: Darker = closer, lighter = farther
• Usage: Quick visualization and inspection

Metadata

• Type: StructuredBlob (JSON)
• Description: Depth statistics and information
• Contains: min_depth, max_depth, mean_depth, median_depth
• Usage: Understanding depth range and distribution

Configuration Tips

Model Selection by Use Case

Real-time AR/VR Applications

• Model Size: "small"
• Input Resolution: 384x384 or 512x512
• Expected Speed: 30-50 FPS on RTX 3060
• Trade-off: Slight accuracy loss for real-time performance

High-Quality 3D Reconstruction

• Model Size: "large"
• Input Resolution: 1024x1024 or original
• Expected Speed: 3-7 FPS on RTX 3060
• Trade-off: Best accuracy, acceptable for offline processing

Balanced Production Use

• Model Size: "base"
• Input Resolution: 512x512 to 768x768
• Expected Speed: 10-20 FPS on RTX 3060
• Trade-off: Good accuracy with practical speed

Edge Deployment

• Model Size: "small"
• Input Resolution: 384x384
• Optimization: Consider ONNX/TensorRT conversion
• Expected Speed: 5-15 FPS on edge devices (Jetson Nano, etc.)

Resolution Recommendations

Low Resolution (384x384)

• Use case: Real-time applications, speed critical
• Quality: Good for general depth understanding
• Speed: Fastest inference
• Memory: Minimal (~500MB VRAM)

Medium Resolution (512x512 to 768x768)

• Use case: Balanced applications, production systems
• Quality: Excellent detail preservation
• Speed: Good (10-30 FPS on mid-range GPU)
• Memory: Moderate (~1-2GB VRAM)

High Resolution (1024x1024+)

• Use case: Highest quality reconstruction, offline processing
• Quality: Maximum detail and accuracy
• Speed: Slower (3-10 FPS)
• Memory: High (~3-6GB VRAM)

Fine-tuning Best Practices

While Depth Anything excels at zero-shot inference, fine-tuning can improve domain-specific accuracy:

When to Fine-tune:

• Consistent camera setup (same intrinsics)
• Specific domain (indoor only, specific robot, etc.)
• Have ground truth depth data (LiDAR, stereo, structured light)
• Need metric depth accuracy (real-world measurements)

Fine-tuning Data Requirements:

• Minimum: 500 paired RGB-depth images
• Recommended: 2000-5000 images
• Diversity: Cover expected scenarios and conditions
• Quality: Accurate ground truth depth essential

Fine-tuning Configuration:

• Start with pre-trained Depth Anything weights
• Low learning rate (1e-6 to 1e-5)
• Focus on output head fine-tuning first
• Monitor validation depth error metrics

Hardware Requirements

Minimum Configuration (Small Model)

• GPU: 4GB VRAM (GTX 1650, RTX 2060)
• RAM: 8GB system memory
• Speed: 10-20 FPS at 512x512

Recommended Configuration (Base Model)

• GPU: 6-8GB VRAM (RTX 3060, RTX 4060)
• RAM: 16GB system memory
• Speed: 15-30 FPS at 512x512

High-End Configuration (Large Model)

• GPU: 10-12GB VRAM (RTX 3080, RTX 4070)
• RAM: 16GB system memory
• Speed: 5-15 FPS at 512x512, 10-30 FPS at 384x384

CPU Inference

• Possible but slow (0.5-5 FPS depending on resolution)
• Small model only practical on CPU
• Not recommended for real-time applications

Common Issues and Solutions

Noisy Depth in Uniform Areas

Problem: Plain walls or textureless regions show grainy depth

Solutions:

1. Apply bilateral filtering to smooth while preserving edges
2. Increase input resolution for better stability
3. Use Base or Large model (better at textureless regions)
4. Acceptable for most applications (post-processing can help)

Incorrect Depth at Object Boundaries

Problem: Depth edges appear blurred or bleeding between objects

Solutions:

1. Use higher input resolution (reduces boundary blur)
2. Apply edge-aware post-processing
3. Upgrade to Large model for sharper boundaries
4. Use depth discontinuity detection for edge refinement

Scale Inconsistency Across Images

Problem: Depth maps have different scales between images

Solutions:

1. Use disparity output for more consistent scale
2. Normalize depth maps to common range (0-1)
3. Fine-tune on calibrated data for metric depth
4. Apply scale alignment post-processing

Reflective Surface Errors

Problem: Mirrors, windows, water show incorrect depth

Solutions:

1. Physical limitation of monocular depth estimation
2. Use semantic segmentation to mask reflective surfaces
3. Apply heuristics (assume far depth for reflective regions)
4. Not solvable without additional sensors

Memory Errors with High Resolution

Problem: Out of memory errors on large images

Solutions:

1. Reduce input resolution (resize before inference)
2. Use Small model instead of Large
3. Process images in tiles and stitch results
4. Increase GPU VRAM or use CPU (slower)

Slow Inference Speed

Problem: Processing too slow for application needs

Solutions:

1. Switch to Small model (3-5x faster than Large)
2. Reduce input resolution
3. Use GPU if running on CPU
4. Enable mixed precision inference (FP16)
5. Batch multiple images together
6. Consider model quantization (INT8)

Example Use Cases

3D Photo Effect Generation

Scenario: Convert 2D photos to animated 3D effects for social media

Configuration:

Model Size: base
Input Resolution: 1024x1024
Output Type: depth

Pipeline:
1. Predict depth map
2. Segment into depth layers
3. Apply parallax animation
4. Render animated output

Why Depth Anything: Excellent detail preservation, works on any photo, no training needed

Expected Results: High-quality 3D effect with natural depth transitions

Autonomous Robot Navigation

Scenario: Mobile robot needs depth for obstacle avoidance

Configuration:

Model Size: small
Input Resolution: 384x384
Output Type: depth
Frame Rate: 30 FPS

Processing:
1. Capture camera frame
2. Predict depth (real-time)
3. Detect obstacles (close depth)
4. Plan collision-free path

Why Depth Anything: Fast inference, robust across environments, consistent predictions

Expected Results: Real-time depth at 30 FPS, reliable obstacle detection

Bokeh Effect for Photography App

Scenario: Mobile app adds portrait mode blur to any photo

Configuration:

Model Size: base
Input Resolution: 768x768
Output Type: depth

Pipeline:
1. Load user photo
2. Predict depth map
3. Segment subject (close depth)
4. Apply gaussian blur by depth
5. Composite final image

Why Depth Anything: High-quality depth for realistic blur, works on any subject

Expected Results: Professional-looking bokeh effect, natural depth-based blur

AR Object Placement

Scenario: AR app places virtual objects in real scenes with correct occlusions

Configuration:

Model Size: small
Input Resolution: 512x512
Output Type: depth
Frame Rate: 30 FPS

Pipeline:
1. Predict depth from camera feed
2. Place virtual object at user-selected location
3. Apply occlusion (real objects in front)
4. Render with correct depth ordering

Why Depth Anything: Real-time performance, accurate occlusions, robust to varied scenes

Expected Results: Realistic AR with proper object interactions and occlusions

Video Depth for Cinematic Effects

Scenario: Film post-production adds depth-based color grading

Configuration:

Model Size: large
Input Resolution: 1920x1080
Output Type: depth

Pipeline:
1. Process each video frame
2. Predict high-quality depth
3. Apply temporal smoothing
4. Depth-based color grading
5. Distance-dependent effects

Why Depth Anything: Highest quality depth, good temporal consistency, professional results

Expected Results: Cinematic depth effects with natural gradation

Comparison with Alternatives

Depth Anything vs MiDaS

Choose Depth Anything when:

• Need state-of-the-art accuracy
• Want better zero-shot generalization
• Require fine detail preservation
• Can use GPU for inference
• Need consistent predictions across domains

Choose MiDaS when:

• Have existing MiDaS pipeline
• Need proven legacy model
• Simpler deployment requirements
• CPU-only inference

Depth Anything vs DPT (Dense Prediction Transformer)

Choose Depth Anything when:

• Want best available zero-shot performance
• Need robust cross-domain generalization
• Require multiple model size options
• Value training on larger, more diverse datasets

Choose DPT when:

• Have specific DPT-trained checkpoints
• Research/academic use aligned with DPT
• Established DPT workflow

Depth Anything Small vs Base vs Large

Choose Small when:

• Real-time performance critical (30+ FPS)
• Edge deployment (Jetson, mobile)
• Limited GPU memory (<4GB)
• Acceptable accuracy trade-off

Choose Base when:

• Balanced speed and accuracy needed
• Production deployment (10-20 FPS)
• Moderate GPU (6-8GB VRAM)
• Most common use cases

Choose Large when:

• Maximum accuracy required
• Offline/batch processing acceptable
• High-end GPU available (10+ GB VRAM)
• Quality more important than speed

Monocular Depth vs Stereo/LiDAR

Choose Monocular (Depth Anything) when:

• Single camera available
• Cost-constrained deployment
• Zero-shot generalization valuable
• Relative depth sufficient

Choose Stereo/LiDAR when:

• Absolute metric depth required
• Can afford additional sensors
• Outdoor long-range needed
• Safety-critical applications (autonomous driving)

Zero-shot vs Fine-tuned

Use Zero-shot (Recommended) when:

• No ground truth depth data available
• Varied scenes and conditions
• Quick deployment needed
• Strong pre-trained performance sufficient

Fine-tune when:

• Consistent camera setup
• Metric depth accuracy required
• Have quality ground truth data (500+ images)
• Domain-specific optimization valuable
• Can invest training time and resources

Advanced Techniques

Temporal Consistency for Video

For video depth prediction with consistent frame-to-frame depth:

1. Per-frame Prediction: Process each frame independently
2. Temporal Smoothing: Apply moving average across frames
3. Optical Flow Alignment: Warp previous depth using flow
4. Weighted Fusion: Combine current prediction with warped previous
5. Result: Smooth, temporally-consistent video depth

Point Cloud Generation

Convert depth map to 3D point cloud:

# Pseudo-code for point cloud generation
depth_map = predict_depth(image)
height, width = depth_map.shape

# Camera intrinsics (focal length, principal point)
fx, fy = focal_lengths
cx, cy = principal_point

# Generate point cloud
for y in range(height):
    for x in range(width):
        z = depth_map[y, x]
        X = (x - cx) * z / fx
        Y = (y - cy) * z / fy
        Z = z
        point_cloud.append([X, Y, Z, color[y, x]])

Depth-guided Image Segmentation

Use depth discontinuities for segmentation:

1. Compute depth gradient (edge detection)
2. Find large depth changes (object boundaries)
3. Combine with appearance-based segmentation
4. Improve boundary accuracy with depth cues

Multi-view Consistency

For multiple views of same scene:

1. Predict depth for each view
2. Back-project to 3D point clouds
3. Align point clouds (ICP or similar)
4. Fuse consistent points
5. Identify and resolve inconsistencies

Depth Inpainting

Fill missing or invalid depth regions:

1. Identify invalid depth areas (reflections, occlusions)
2. Use surrounding valid depth
3. Apply depth-aware inpainting
4. Maintain geometric consistency
5. Result: Complete, plausible depth maps