IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty (2210.03676v1)

Abstract: Single image surface normal estimation and depth estimation are closely related problems as the former can be calculated from the latter. However, the surface normals computed from the output of depth estimation methods are significantly less accurate than the surface normals directly estimated by networks. To reduce such discrepancy, we introduce a novel framework that uses surface normal and its uncertainty to recurrently refine the predicted depth-map. The depth of each pixel can be propagated to a query pixel, using the predicted surface normal as guidance. We thus formulate depth refinement as a classification of choosing the neighboring pixel to propagate from. Then, by propagating to sub-pixel points, we upsample the refined, low-resolution output. The proposed method shows state-of-the-art performance on NYUv2 and iBims-1 - both in terms of depth and normal. Our refinement module can also be attached to the existing depth estimation methods to improve their accuracy. We also show that our framework, only trained for depth estimation, can also be used for depth completion. The code is available at https://github.com/baegwangbin/IronDepth.

• The paper introduces an iterative pipeline that refines single-view depth estimates using surface normals to enforce geometric consistency.
• The paper leverages uncertainty measures to dynamically weight contributions from image regions, reducing errors in textureless and occluded areas.
• The method shows potential for real-time applications in autonomous driving, robotics, and augmented reality through enhanced 3D scene clarity.

IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty

The paper "IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty" presents a novel approach to the problem of estimating depth from a single image. The authors from the University of Cambridge propose a technique that iteratively refines depth predictions by leveraging surface normals and associated uncertainty measures to enhance the precision of the derived 3D point clouds.

Depth estimation from single images is a critical task with significant applications in computer vision, including autonomous driving, robotic navigation, and augmented reality. Traditional approaches often struggle with the inherent ambiguity in depth estimation due to the limited perspective offered by a single viewpoint. The work done by Bae, Budvytis, and Cipolla addresses this challenge by incorporating surface normals into the refinement process, thereby supporting more accurate depth predictions.

The IronDepth method introduces an iterative refinement pipeline where initial depth predictions undergo successive updates. This iterative process is guided by surface normals, which provide an orientation-based constraint to enforce geometric consistency across the depth map. Such a mechanism is particularly effective in addressing local inaccuracies where depth values may otherwise diverge due to isolated errors in the initial prediction. Moreover, the incorporation of uncertainty estimation allows for a more robust adjustment, by weighting the contributions of different regions based on their respective confidence levels.

A notable strength of the IronDepth approach lies in its ability to reduce depth estimation errors in challenging scenarios, such as textureless surfaces and occlusions. Preliminary results, as demonstrated in the accompanying video, exhibit enhanced clarity and structural integrity of the reconstructed 3D scenes following iterative refinement. This suggests a tangible improvement over methods that rely solely on initial estimations without subsequent refinement.

The implications of this research are significant for fields requiring precise depth information from monocular setups. From a theoretical perspective, the integration of uncertainty measures introduces a compelling direction for future exploration, as it provides a mechanism to harmonize multiple sources of information dynamically. Practically, the method offers a prospective module for real-time applications where depth estimation must be both rapid and reliable.

Future developments in this area of research could explore the expansion of IronDepth's iterative framework to incorporate other contextual cues, such as semantic segmentation, to further enrich the depth estimation process. Moreover, the scalability of the proposed method to more complex scenes with diverse lighting conditions and surface reflectivity could be investigated to enhance its applicability across various domains. Such advancements would continue to bridge the gap between single-view depth estimation challenges and the practical demands of real-world applications.