DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation (2303.05021v4)

Abstract: Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image. Current methods typically model this problem as a regression or classification task. We propose DiffusionDepth, a new approach that reformulates monocular depth estimation as a denoising diffusion process. It learns an iterative denoising process to `denoise' random depth distribution into a depth map with the guidance of monocular visual conditions. The process is performed in the latent space encoded by a dedicated depth encoder and decoder. Instead of diffusing ground truth (GT) depth, the model learns to reverse the process of diffusing the refined depth of itself into random depth distribution. This self-diffusion formulation overcomes the difficulty of applying generative models to sparse GT depth scenarios. The proposed approach benefits this task by refining depth estimation step by step, which is superior for generating accurate and highly detailed depth maps. Experimental results on KITTI and NYU-Depth-V2 datasets suggest that a simple yet efficient diffusion approach could reach state-of-the-art performance in both indoor and outdoor scenarios with acceptable inference time.

• The paper reformulates monocular depth estimation as a denoising diffusion problem to iteratively refine depth maps.
• It introduces a self-diffusion process that overcomes sparse ground truth data by enhancing overall depth map organization.
• State-of-the-art results on KITTI and NYU-Depth-V2 demonstrate the method's efficiency in capturing both coarse and fine depth details.

An Expert Overview of "DiffusionDepth: Diffusion-based Self-Refinement Approach for Monocular Depth Estimation"

The paper "DiffusionDepth: Diffusion-based Self-Refinement Approach for Monocular Depth Estimation" presents a novel method for estimating depth from monocular images by employing a diffusion-based framework. This approach seeks to address some of the inherent challenges in monocular depth estimation, which is typically treated as a regression or classification task. The research introduces a fresh perspective on this problem by leveraging diffusion denoising processes to iteratively refine depth maps with visual guidance, achieving high-resolution and accurate depth estimations.

Key Contributions

1. Diffusion-Based Reformulation: The authors propose a unique method by reformulating monocular depth estimation as a denoising diffusion problem. Traditional tasks typically focus on regression or classification, which often suffer from overfitting and inadequate object detail generation. By employing a diffusion model, the methodology iteratively refines an initial random depth distribution into a coherent depth map, guided by monocular visual conditions.
2. Self-Diffusion Process: The paper addresses the issue of sparse ground truth (GT) depth values in datasets such as KITTI, where only a small fraction of pixels have corresponding depth measurements. The self-diffusion process involves adding noise to a refined depth latent rather than sparse GT, allowing the model to organize the entire depth map iteratively rather than focusing on known parts alone. This innovation is noteworthy for its potential to overcome common challenges in generative model applications to 3D perception tasks.
3. State-of-the-Art Performance: The method is evaluated on standard benchmarks like KITTI and NYU-Depth-V2, demonstrating state-of-the-art performance across both indoor and outdoor scenarios. The approach achieves impressive results with competitive inference times due to its efficient design and the inherent properties of the diffusion model to enhance both coarse and fine details of the depth map.
4. Theoretical and Practical Implications: The research provides insights into the application of generative models to depth estimation, offering a promising alternative to traditional regression techniques. It also suggests that the iterative refinement capabilities of diffusion models can improve prediction accuracy and detail capture, making them applicable to other 3D vision tasks.

Methodology and Evaluation

The DiffusionDepth framework utilizes a latent space, encoded by dedicated depth encoders and decoders, to perform denoising tasks iteratively. It integrates hierarchical aggregation and heterogeneous interaction (HAHI) for scalable feature extraction across different monocular visual scales. Through this multi-scale feature aggregation, the model enhances visual condition inputs, crucial for guiding the depth refinement process.

The authors provide extensive evaluations on the KITTI and NYU-Depth-V2 datasets, showcasing the model's ability to outperform current techniques. On the KITTI dataset, the approach reaches a performance level with RMSE of 1.418, while on the NYU-Depth-V2, it achieves an RMSE of 0.298, signifying substantial improvements in relation to existing methods.

Future Directions

The introduction of diffusion models into depth estimation by this work opens up new avenues for research in AI and vision tasks. There is an opportunity to explore more complex scene understanding tasks using diffusion models, potentially improving their generalization and robustness. Furthermore, integrating such models with more sophisticated visual backbones and constraints might further enhance depth prediction performance in diverse environments.

In conclusion, the paper makes significant strides in reformulating monocular depth estimation, offering a robust alternative through the diffusion-denoising process. It extends the applicability of generative models to depth tasks and sets a foundation for future explorations in 3D visual perception.