Adaptive Surface Normal Constraint for Accurate Geometric Estimation from Monocular Images

Core Concepts

The core message of this paper is that by incorporating geometric context that encodes local 3D geometric variations, the proposed Adaptive Surface Normal (ASN) constraint can effectively correlate depth estimation and normal estimation, enabling the generation of high-quality 3D geometry from monocular images.

Abstract

The paper introduces a novel approach to jointly predict depth and surface normal from single images, with a focus on incorporating geometric context to improve the consistency between the different geometric properties. Key highlights: The proposed ASN constraint adaptively determines the reliable local geometry indicated by the learned geometric context to correlate depth and surface normal estimation. The learned geometric context is utilized to enhance the predicted normals by prioritizing regions with high-frequency geometric variations, enabling the network to accurately capture intricate and detailed geometric information. The joint estimation of depth and normal, guided by the geometric context, enables the generation of high-quality 3D geometry from monocular images, outperforming state-of-the-art methods on both indoor and outdoor datasets. The paper first reviews related work on monocular depth and normal estimation. It then elaborates on the details of the proposed ASN constraint and the geometric context guided normal estimation approach. Extensive experiments are conducted on indoor and outdoor datasets, demonstrating the superiority of the proposed method in terms of depth estimation, surface normal prediction, and point cloud reconstruction quality.

Stats

The paper evaluates the proposed method on the following datasets: NYUD-V2: An indoor dataset with 464 scenes, 249 for training and 215 for testing. ScanNet: An indoor dataset with 100 test scenes and 2,167 test images. MVS-SYNTH: An outdoor synthetic dataset with 6,000 training and 960 test images. SVERS: A synthetic outdoor dataset with vehicle-end camera viewpoints, with 6,372 training and 710 test images.

Quotes

"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." "Our method can not only accurately capture sufficient geometric context information but also be highly efficient for computation."

Key Insights Distilled From

Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images

by Xiaoxiao Lon... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2402.05869.pdf

Deeper Inquiries

How can the proposed method be extended to handle dynamic scenes with moving objects

To extend the proposed method to handle dynamic scenes with moving objects, we can incorporate motion estimation techniques to account for the movement of objects in the scene. By integrating optical flow algorithms or object tracking methods, we can track the motion of objects and adjust the depth and normal estimation accordingly. This would involve updating the geometric context dynamically based on the movement of objects in the scene. Additionally, utilizing recurrent neural networks or temporal convolutional networks can help capture temporal dependencies in the scene, enabling the model to adapt to changes over time.

What are the potential limitations of the ASN constraint and how can they be addressed

One potential limitation of the Adaptive Surface Normal (ASN) constraint is its reliance on the local plane assumption, which may not hold true in all scenarios, especially in regions with complex geometry or sharp changes. To address this limitation, we can incorporate higher-order geometric constraints or geometric priors to improve the accuracy of normal estimation in challenging areas. Additionally, exploring more sophisticated sampling strategies or incorporating attention mechanisms can help the model focus on regions with intricate geometric details, enhancing the robustness of the method.

How can the learned geometric context be leveraged for other vision tasks beyond depth and normal estimation

The learned geometric context can be leveraged for various other vision tasks beyond depth and normal estimation. For instance, in semantic segmentation, the geometric context can provide valuable information about the spatial relationships between objects, aiding in more accurate segmentation. In object detection, the geometric context can help in understanding the 3D structure of objects and improving localization accuracy. Furthermore, in image registration tasks, the geometric context can assist in aligning images based on their geometric properties, leading to more precise registration results. By incorporating the learned geometric context into these tasks, we can enhance the performance and robustness of the models across a wide range of computer vision applications.