Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images (2402.05869v2)

Abstract: We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness.

• The paper demonstrates that the Adaptive Surface Normal constraint effectively bridges the gap between depth and normal estimation from single-shot monocular images.
• It utilizes dynamic selection of local geometric context to enhance accuracy, especially in regions with complex transitions like shape boundaries and corners.
• Robust evaluations on diverse indoor and outdoor datasets validate its superiority by consistently outperforming state-of-the-art methods.

Adaptive Surface Normal Constraint Enhances Depth and Normal Estimation from Monocular Images

In the burgeoning field of computer vision, the interpretation and reconstruction of three-dimensional geometry from two-dimensional images remain a pivotal challenge. This challenge is acutely felt in single-shot monocular image scenarios due to the inherent information loss when projecting 3D realities onto 2D surfaces. Traditional methodologies have typically treated the estimations of depth and surface normals independently, frequently leading to inconsistencies between the reconstructed geometric properties. Acknowledging this issue, a paper by Xiaoxiao Long et al. introduces an innovative approach known as the Adaptive Surface Normal (ASN) constraint, aiming to bridge this gap and enhance geometric estimation quality significantly.

Breaking Down the Innovation: ASN Constraint

The ASN constraint operates by extracting geometric context from a given monocular image, effectively encoding variations present within the scene. This context is then dynamically utilized to establish a reliable correlation between depth estimation and geometric constraints, significantly boosting the accuracy and robustness of 3D geometry generation. One of the standout features of ASN is its ability to dynamically select the most reliable local geometry from a pool of candidates, using a novel surface normal estimation leveraged by the geometric context.

This process addresses a critical issue observed in previous methodologies - the challenge of determining reliable local geometry, especially in regions with complex transitions like shape boundaries or corners. The Adaptive Surface Normal constraint overcomes this through a two-pronged strategy: evaluating the validity of candidate normals through geometric context, and weighting them accordingly to determine the most credible normal estimation.

Unprecedented Results on Diverse Datasets

The superiority of the ASN constraint method is empirically validated across various indoor and outdoor datasets, showcasing its efficiency and robustness in geometric estimation. When compared to state-of-the-art methods, ASN consistently outperforms across all evaluation metrics, notably in its ability to maintain geometric consistency and detail richness even in challenging scenarios.

A Closer Look Through Extensive Evaluations

The paper further explores the intricacies of the ASN approach through extensive evaluations and ablation studies. These delve into aspects like the effectiveness of geometric context guidance, the optimal number of sampled local planes for normal estimation, and the impact of the window size used in local geometry sampling. Significantly, the studies underscore ASN’s capacity to adaptively refine depth estimation using geometric properties, ensuring an unprecedented level of detail and accuracy in the reconstructed 3D geometry.

Broader Implications and Future Directions

Beyond its immediate contributions, the paper paves the way for future research in geometric estimation from monocular images. The introduction of the ASN constraint not only enhances current understanding and methodologies in depth and surface normal estimation but also opens avenues for the development of more sophisticated, context-aware algorithms that can further bridge the gap between 2D imagery and 3D geometric reconstruction.

In conclusion, the Adaptive Surface Normal constraint presents a significant leap forward in our quest for accurate and reliable 3D geometry estimation from monocular images. Through its innovative approach to leveraging geometric context and dynamic adaptability, ASN sets a new benchmark in the field, with vast potential applications ranging from augmented reality to autonomous navigation.

For more detailed insights and technical nuances of this groundbreaking work, enthusiasts and researchers are encouraged to delve into the full paper and explore its repository at https://github.com/xxlong0/ASNDepth.