Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens
Proposed a method using Calibration Tokens to extend monocular depth estimators to fisheye cameras, significantly improving depth estimation accuracy.
Key Findings
Methodology
The method introduces Calibration Tokens to adjust fisheye image latent embeddings to align with perspective image distributions. Using the attention mechanism of vision transformers, Calibration Tokens are part of the input, modulating latent embeddings to reduce covariate shift. The entire process requires no retraining or fine-tuning of the model.
Key Results
- On ScanNet++ and KITTI-360 datasets, models with Calibration Tokens improved RMSE by 12% and 15%, respectively.
- Compared to existing DepthAnyCamera and FoVA-Depth methods, UniDepth with Calibration Tokens outperformed in indoor and outdoor scenarios, reducing RMSE by 11% and 14%, respectively.
- Experiments show Calibration Tokens effectively extend depth estimation capabilities from perspective to fisheye images without additional camera intrinsics.
Significance
This study addresses the poor performance of monocular depth estimators on fisheye images, proposing a Calibration Token method that requires no model retraining, significantly reducing computational costs and operational complexity. This method provides new solutions for wide-angle camera applications in fields like autonomous driving and extended reality.
Technical Contribution
The technical contribution lies in proposing a lightweight Calibration Token mechanism that adapts to different camera intrinsics and distortions without altering the model architecture. The method uses self-supervised learning, ensuring training stability and accuracy through inverse transformations and high-fidelity depth map self-supervised loss.
Novelty
This method is the first to introduce Calibration Tokens in monocular depth estimation, addressing covariate shift in fisheye images by adjusting latent embeddings, avoiding distortions from spatial transformations compared to traditional image reprojection methods.
Limitations
- The method's performance under extreme lighting conditions has not been validated, which may affect depth estimation accuracy.
- Training of Calibration Tokens relies on large perspective image datasets, which may not be suitable for data-scarce domains.
- In some complex scenarios, Calibration Tokens may not completely eliminate all distortion effects.
Future Work
Future research directions include exploring robustness under different lighting conditions and further optimizing Calibration Token performance in more complex scenarios. Additionally, integrating other sensor data (e.g., LiDAR) to improve depth estimation accuracy is a potential research direction.
AI Executive Summary
Monocular depth estimation is crucial in fields like autonomous driving and extended reality, but existing estimators perform poorly on fisheye images due to covariate shifts from camera calibration changes. This paper proposes a novel method by introducing Calibration Tokens to adjust fisheye image latent embeddings to align with perspective image distributions, improving depth estimation accuracy without retraining the model.
The method leverages the attention mechanism of vision transformers, with Calibration Tokens as part of the input to modulate latent embeddings and reduce covariate shift. Using self-supervised learning, the method ensures training stability and accuracy through inverse transformations and high-fidelity depth map self-supervised loss. Experimental results show models with Calibration Tokens significantly outperform existing methods on ScanNet++ and KITTI-360 datasets.
This research provides new solutions for wide-angle camera applications in fields like autonomous driving and extended reality, significantly reducing computational costs and operational complexity. However, the method's performance under extreme lighting conditions has not been validated. Future research directions include exploring robustness under different lighting conditions and further optimizing Calibration Token performance in more complex scenarios.
Deep Analysis
Background
Monocular depth estimation is a crucial task in computer vision, widely used in fields like autonomous driving and extended reality. Traditional depth estimators are primarily trained on perspective images, but perform poorly on fisheye images due to changes in camera calibration parameters. Existing methods often require retraining models or complex image reprojection, increasing computational costs and operational complexity.
Core Problem
The core problem is how to extend the capabilities of monocular depth estimators to fisheye images without retraining the model. Fisheye images, due to their wide-angle nature, often come with significant distortions, challenging the generalization ability of depth estimators. Solving this problem is crucial for improving the applicability of depth estimation and reducing computational costs.
Innovation
The core innovation is the introduction of Calibration Tokens, which adjust fisheye image latent embeddings to align with perspective image distributions. This method avoids complex image reprojection, preventing distortions from spatial transformations. Additionally, using self-supervised learning ensures training stability and accuracy through inverse transformations and high-fidelity depth map self-supervised loss.
Methodology
- �� Introduce Calibration Tokens as part of the input to adjust latent embeddings.
- �� Use the attention mechanism of vision transformers to reduce covariate shift.
- �� Employ self-supervised learning with inverse transformations and high-fidelity depth map self-supervised loss to ensure training stability and accuracy.
- �� Conduct experiments on ScanNet++ and KITTI-360 datasets for validation.
Experiments
The experimental design includes validation on ScanNet++ and KITTI-360 datasets, using baseline models MiDaS, DepthAnything, and UniDepth. Evaluation metrics are RMSE and δ1, with 8 Calibration Tokens used in experiments. Comparisons with existing methods validate the effectiveness of Calibration Tokens in improving depth estimation accuracy.
Results
Experimental results show models with Calibration Tokens improved RMSE by 12% and 15% on ScanNet++ and KITTI-360 datasets, respectively. Compared to existing DepthAnyCamera and FoVA-Depth methods, UniDepth with Calibration Tokens outperformed in indoor and outdoor scenarios, reducing RMSE by 11% and 14%, respectively.
Applications
The method can be directly applied in fields like autonomous driving and extended reality, especially in scenarios requiring wide-angle camera image processing. As the method requires no model retraining or complex image reprojection, it significantly reduces computational costs and operational complexity.
Limitations & Outlook
The method's performance under extreme lighting conditions has not been validated, which may affect depth estimation accuracy. Additionally, the training of Calibration Tokens relies on large perspective image datasets, which may not be suitable for data-scarce domains. In some complex scenarios, Calibration Tokens may not completely eliminate all distortion effects.
Plain Language Accessible to non-experts
Imagine you're in a kitchen with lots of different shaped pots and tools. Traditional depth estimators are like a chef who only knows how to use standard pots. When faced with a large, oddly-shaped pot (fisheye image), the chef doesn't know what to do. Our Calibration Tokens are like a universal pot mat, allowing the chef to cook delicious dishes on any pot. By adjusting the mat's position and angle, the chef ensures the ingredients are evenly heated, unaffected by the pot's shape. This way, whether it's a standard pot or an oddly-shaped one, the chef can easily handle it and make tasty dishes.
ELI14 Explained like you're 14
Hey kids! Imagine you're playing a super cool game where your character needs to jump and run on different maps. Some maps are simple, like the ones you usually play, but some are weird with complex terrains. Our Calibration Tokens are like a magic tool that helps your character jump easily on these complex maps. It's like a super smart navigation system that automatically adjusts your character's moves, so you can score high on any map! Isn't that awesome?
Glossary
Monocular Depth Estimation
A technique to estimate scene depth using a single camera image.
Used in the paper for depth estimation of perspective and fisheye images.
Calibration Tokens
A lightweight mechanism to adjust fisheye image latent embeddings.
Part of the input to help the model adapt to different camera intrinsics.
Self-supervised Learning
A method of training using data's own information without manual labels.
Used to train Calibration Tokens, ensuring training stability and accuracy.
Vision Transformer
A vision model based on transformer architecture for image processing.
Utilizes its attention mechanism to adjust latent embeddings.
Covariate Shift
A phenomenon where model performance degrades due to changes in input data distribution.
Causes depth estimation errors between fisheye and perspective images.
Open Questions Unanswered questions from this research
- 1 How to improve depth estimation robustness under extreme lighting conditions? Current methods' performance in these conditions is unverified.
- 2 How to effectively train Calibration Tokens in data-scarce domains?
- 3 How to further optimize Calibration Tokens to eliminate all distortion effects in complex scenarios?
Applications
Immediate Applications
Autonomous Driving
Apply Calibration Tokens in autonomous driving to improve depth estimation accuracy of fisheye cameras, ensuring vehicle safety in complex environments.
Extended Reality
Use Calibration Tokens in extended reality applications to process wide-angle camera images, enhancing user experience.
Long-term Vision
Smart City Surveillance
In the future, it can be used in smart city surveillance systems to process data from various camera types, improving monitoring accuracy and efficiency.
Abstract
We propose a method to extend foundational monocular depth estimators (FMDEs), trained on perspective images, to fisheye images. Despite being trained on tens of millions of images, FMDEs are susceptible to the covariate shift introduced by changes in camera calibration (intrinsic, distortion) parameters, leading to erroneous depth estimates. Our method aligns the distribution of latent embeddings encoding fisheye images to those of perspective images, enabling the reuse of FMDEs for fisheye cameras without retraining or finetuning. To this end, we introduce a set of Calibration Tokens as a light-weight adaptation mechanism that modulates the latent embeddings for alignment. By exploiting the already expressive latent space of FMDEs, we posit that modulating their embeddings avoids the negative impact of artifacts and loss introduced in conventional recalibration or map projection to a canonical reference frame in the image space. Our method is self-supervised and does not require fisheye images but leverages publicly available large-scale perspective image datasets. This is done by recalibrating perspective images to fisheye images, and enforcing consistency between their estimates during training. We evaluate our approach with several FMDEs, on both indoors and outdoors, where we consistently improve over state-of-the-art methods using a single set of tokens for both. Code available at: https://github.com/JungHeeKim29/calibration-token; https://github.com/Suchisrit/CalibrationTokens.