Visual Autoregressive Modelling for Monocular Depth Estimation
Proposed a monocular depth estimation method using visual autoregressive (VAR) priors, achieving state-of-the-art indoor performance with only 74K samples.
Key Findings
Methodology
The method utilizes a visual autoregressive (VAR) model, introducing a scale-wise conditional upsampling mechanism and classifier-free guidance. It performs inference in ten fixed autoregressive stages, using the Switti model for pretraining and fine-tuning on Hypersim and vKITTI datasets.
Key Results
- Achieved state-of-the-art indoor performance on NYUv2 and ScanNet with AbsRel of 6.4 and 7.9, and δ1 of 94.8% and 93.4%, respectively.
- Performed well on outdoor datasets KITTI, ETH3D, and DIODE with AbsRel of 10.4, 8.1, and 22.3, respectively.
- VAR models offer advantages in data requirements and computational overhead compared to diffusion-based models.
Significance
This research introduces a new paradigm for monocular depth estimation using autoregressive priors, significantly reducing data requirements and enhancing adaptability to 3D vision tasks.
Technical Contribution
Introduced a novel autoregressive generative model with scale-wise conditional upsampling and classifier-free guidance, offering distinct theoretical and engineering possibilities compared to existing diffusion models.
Novelty
First to apply a large-scale text-to-image VAR model to depth estimation, introducing a unique scale-wise conditional upsampling mechanism, significantly reducing data needs.
Limitations
- Performance slightly lags behind some CNN-based models in outdoor scenes, possibly due to domain differences in training datasets.
- Inference speed remains slow, though faster than diffusion models.
Future Work
Future work could explore fine-tuning on more real-world datasets and improving inference speed and model generalization.
AI Executive Summary
Monocular depth estimation is a crucial task in computer vision, where traditional methods rely on large datasets and complex training processes, limiting their adaptability to diverse applications.
This paper proposes a monocular depth estimation method based on visual autoregressive (VAR) priors, introducing a scale-wise conditional upsampling mechanism and classifier-free guidance, significantly reducing data requirements with only 74K samples for fine-tuning.
Experimental results demonstrate state-of-the-art performance on indoor datasets NYUv2 and ScanNet, and strong performance on outdoor datasets KITTI, ETH3D, and DIODE, showcasing its potential in 3D vision tasks. While slightly behind CNN methods in some outdoor scenarios, its data scalability and adaptability offer new directions for future research.
Deep Analysis
Background
Monocular depth estimation involves predicting scene depth from a single 2D image. Traditional methods rely on supervised and self-supervised learning, the former requiring extensive labeled data, the latter reducing annotation needs through geometric constraints. Recently, generative models like diffusion models have been applied to depth estimation, but they are computationally intensive and data-hungry.
Core Problem
Monocular depth estimation faces challenges such as scale ambiguity, perspective ambiguity, and occlusions. Existing methods are bottlenecked by data requirements and computational overhead, hindering their application in diverse scenarios.
Innovation
This paper innovatively applies visual autoregressive (VAR) models to depth estimation, introducing a scale-wise conditional upsampling mechanism and classifier-free guidance, reducing data needs and enhancing model adaptability.
Methodology
- �� Pretrain using the Switti model, adapting it for depth estimation.
- �� Introduce a scale-wise conditional upsampling mechanism to propagate geometric cues across resolutions.
- �� Design a classifier-free guidance rule to balance contributions from autoregressive priors and conditional upsamplers.
- �� Propose a re-encoding strategy to support intermediate predictions.
Experiments
Fine-tune on Hypersim and vKITTI datasets, test on NYUv2, ScanNet, KITTI, ETH3D, and DIODE. Evaluation metrics include AbsRel and δ1, with baselines being diffusion and CNN models.
Results
On NYUv2, AbsRel is 6.4, δ1 is 94.8%; on ScanNet, AbsRel is 7.9, δ1 is 93.4%. On KITTI, AbsRel is 10.4, δ1 is 90.1%. VAR models offer advantages in data requirements and computational overhead compared to diffusion-based models.
Applications
The method can be applied in autonomous driving, robotic vision, and augmented reality, especially in data-limited scenarios.
Limitations & Outlook
Performance slightly lags behind some CNN-based models in outdoor scenes, possibly due to domain differences in training datasets. Inference speed remains slow, though faster than diffusion models. Future improvements could include fine-tuning on more real-world datasets and improving inference speed.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have a recipe but not all the ingredients. Traditional methods are like needing all ingredients to make the perfect dish, while our VAR method is like creating a delicious dish with the ingredients you have. Our method uses a small amount of data (ingredients) and some clever techniques (upsampling mechanism and classifier-free guidance) to successfully make a tasty dish (accurate depth estimation). It's like achieving the maximum effect with limited resources.
ELI14 Explained like you're 14
Imagine you're playing a game where you have to guess the 3D shape from a picture. Traditional methods are like needing lots of hints to guess right, while our new method is like needing only a few hints to get the answer. Our method uses a technique called VAR, like a superpower in the game, helping you guess faster and more accurately. Although it's not as good in some levels as other methods, it's already pretty awesome!
Glossary
Visual Autoregressive
A generative model that predicts images by sequentially predicting different resolution levels.
Used for monocular depth estimation through multi-scale prediction.
Scale-wise Conditional Upsampling
A mechanism that improves depth estimation accuracy by propagating geometric cues across resolutions.
Enhances geometric information propagation in VAR models.
Classifier-free Guidance
A strategy to optimize predictions by balancing contributions from autoregressive priors and conditional upsamplers.
Used to control model predictions at different stages.
Switti Model
A large-scale text-to-image VAR model used for pretraining.
Serves as the foundation for fine-tuning the VAR model.
Hypersim Dataset
A synthetic dataset containing 461 indoor scenes used for model fine-tuning.
Used to train and evaluate the performance of the VAR model.
Open Questions Unanswered questions from this research
- 1 How to validate VAR model performance on larger-scale real-world datasets?
- 2 How to further improve VAR model inference speed for real-time applications?
Applications
Immediate Applications
Autonomous Driving
In autonomous driving, VAR models can be used for real-time depth estimation, enhancing vehicle environmental perception.
Long-term Vision
Augmented Reality
VAR models can be applied in augmented reality to achieve more realistic scene rendering, enhancing user experience.
Abstract
We propose a monocular depth estimation method based on visual autoregressive (VAR) priors, offering an alternative to diffusion-based approaches. Our method adapts a large-scale text-to-image VAR model and introduces a scale-wise conditional upsampling mechanism with classifier-free guidance. Our approach performs inference in ten fixed autoregressive stages, requiring only 74K synthetic samples for fine-tuning, and achieves competitive results. We report state-of-the-art performance in indoor benchmarks under constrained training conditions, and strong performance when applied to outdoor datasets. This work establishes autoregressive priors as a complementary family of geometry-aware generative models for depth estimation, highlighting advantages in data scalability, and adaptability to 3D vision tasks. Code available at "https://github.com/AmirMaEl/VAR-Depth".