[Paper Review] 3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning

Mi Tian, Qiong Nie+1 more|arXiv (Cornell University)|May 13, 2020

Robotics and Sensor-Based Localization36 references4 citations

TL;DR

This paper proposes a compact deep learning network for absolute camera pose regression by integrating a 3D scene geometry-aware constraint through pixel-level photometric loss and image-level structural similarity loss, leveraging motion, depth, and image content. The method achieves significant improvements in both localization accuracy and convergence speed across diverse indoor and outdoor scenes compared to state-of-the-art approaches.

ABSTRACT

Camera localization is a fundamental and key component of autonomous driving vehicles and mobile robots to localize themselves globally for further environment perception, path planning and motion control. Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods. In this work, we propose a compact network for absolute camera pose regression. Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents. We add this constraint as a regularization term to our proposed network by defining a pixel-level photometric loss and an image-level structural similarity loss. To benchmark our method, different challenging scenes including indoor and outdoor environment are tested with our proposed approach and state-of-the-arts. And the experimental results demonstrate significant performance improvement of our method on both prediction accuracy and convergence efficiency.

Motivation & Objective

• To improve the accuracy and efficiency of absolute camera pose regression in complex 3D environments.
• To bridge the gap between end-to-end deep learning and traditional 3D-geometry-based methods by incorporating geometric priors.
• To develop a compact network that effectively utilizes motion, depth, and image content for robust camera localization.
• To benchmark performance across challenging indoor and outdoor scenes under real-world conditions.
• To enhance convergence speed and prediction robustness through geometric regularization in deep neural networks.

Proposed method

• The method introduces a 3D scene geometry-aware constraint by combining pixel-level photometric loss and image-level structural similarity loss.
• Photometric loss enforces consistency between predicted and ground-truth image reconstructions using depth and motion information.
• Structural similarity loss preserves high-level image structures across views to improve feature alignment.
• The constraint is integrated as a regularization term in a compact convolutional neural network for end-to-end camera pose regression.
• Motion and depth data are used to synthesize warped images for supervision during training.
• The network is trained end-to-end with joint optimization of pose regression and geometric consistency losses.

Experimental results

Research questions

• RQ1 Can a deep learning-based camera localization system achieve higher accuracy by incorporating 3D scene geometry constraints?
• RQ2 How does the integration of photometric and structural similarity losses affect convergence speed and robustness?
• RQ3 Does the proposed method outperform state-of-the-art approaches in both indoor and outdoor environments?
• RQ4 To what extent does the use of motion, depth, and image content improve generalization across diverse scenes?
• RQ5 Can a compact network with geometric regularization achieve performance comparable to or better than traditional 3D-geometry methods?

Key findings

• The proposed method achieves significant performance improvements in both prediction accuracy and convergence efficiency on challenging indoor and outdoor scenes.
• The integration of 3D scene geometry-aware constraints leads to more robust and precise camera pose estimation compared to baseline deep learning models.
• The method demonstrates superior generalization across diverse environments, including complex indoor and outdoor settings.
• The use of combined photometric and structural similarity losses enhances feature learning and reduces optimization instability.
• The compact network design enables efficient inference while maintaining high accuracy, outperforming existing end-to-end approaches.
• The method was accepted for presentation at ICRA 2020, indicating recognition within the robotics and computer vision community.