Wide-Baseline Relative Camera Pose Estimation with Directional Learning (2106.03336v1)

Abstract: Modern deep learning techniques that regress the relative camera pose between two images have difficulty dealing with challenging scenarios, such as large camera motions resulting in occlusions and significant changes in perspective that leave little overlap between images. These models continue to struggle even with the benefit of large supervised training datasets. To address the limitations of these models, we take inspiration from techniques that show regressing keypoint locations in 2D and 3D can be improved by estimating a discrete distribution over keypoint locations. Analogously, in this paper we explore improving camera pose regression by instead predicting a discrete distribution over camera poses. To realize this idea, we introduce DirectionNet, which estimates discrete distributions over the 5D relative pose space using a novel parameterization to make the estimation problem tractable. Specifically, DirectionNet factorizes relative camera pose, specified by a 3D rotation and a translation direction, into a set of 3D direction vectors. Since 3D directions can be identified with points on the sphere, DirectionNet estimates discrete distributions on the sphere as its output. We evaluate our model on challenging synthetic and real pose estimation datasets constructed from Matterport3D and InteriorNet. Promising results show a near 50% reduction in error over direct regression methods.

• The paper introduces DirectionNet, a novel method for wide-baseline relative camera pose estimation that predicts discrete distributions over camera poses instead of direct regression.
• DirectionNet demonstrates ~50% error reduction over direct regression and outperforms prior methods on challenging wide-baseline datasets.
• Learning camera poses through discrete distributions enhances robustness and accuracy in difficult vision scenarios, indicating the value of modeling pose uncertainty.

Overview of Wide-Baseline Relative Camera Pose Estimation with Directional Learning

The paper "Wide-Baseline Relative Camera Pose Estimation with Directional Learning" proposes a novel approach to improve the accuracy and robustness of relative camera pose estimation, particularly in challenging scenarios characterized by wide baselines, large camera motions, and low image overlap. Traditional regression methods for pose estimation struggle with these conditions despite access to extensive supervised datasets. The authors introduce DirectionNet, an innovative model that predicts discrete distributions over camera poses to tackle the limitations of direct regression methods.

DirectionNet Framework

DirectionNet approaches pose estimation by decomposing the relative pose problem into a set of directional predictions in a 5D space, separating the camera rotation and translation into discrete 3D direction vectors. The key components of DirectionNet are:

• Directional Parameterization: Relative poses are represented by the 3D rotation and translation, parameterized into 3D vectors that are mapped onto the sphere $S^2$. This reduces the dimensional complexity and makes the problem tractable for neural networks.
• Spherical Distribution Estimation: Instead of direct regression, DirectionNet predicts probability distributions over directions on $S^2$. By estimating discrete distributions, it leverages dense, structured supervision, which is more effective than direct regression.
• Expectation-based Prediction: The expected value of the spherical distribution provides the pose estimate, enabling differentiation and avoidance of the grid resolution issues associated with $\operatorname{argmax}$.

Model Evaluation and Results

DirectionNet was empirically validated on synthetic and real datasets derived from InteriorNet and Matterport3D, testbeds known for their challenging wide-baseline scenarios. The experiments demonstrated that DirectionNet achieves a ~50% reduction in error compared to direct regression methods, outperforming not only traditional feature-based methods (e.g., SIFT+RANSAC) but also contemporary learning-based approaches like SuperGlue.

Implications and Future Work

The paper indicates that learning camera poses through discrete distributions over keypoints or poses can significantly increase model robustness and accuracy in wide-baseline settings. This insight can influence the design of future computer vision models, especially in applications like 3D reconstruction and camera localization. The improvement from using a probabilistic approach is profound because it suggests that explicitly modeling pose uncertainty can enhance a model's performance on challenging datasets.

Potential future work may include:

• Integration with newer architectures: Leveraging advances in vision transformers or other novel architectures could further enhance DirectionNet's efficacy.
• Augmentation with additional modalities: Incorporating other sensory modalities, such as depth data or multi-spectral imaging, could provide richer input for more accurate pose estimation.
• Domain generalization experiments: Exploring how DirectionNet can be adapted to outdoor environments or fine-tuned across varying domains can extend its practical applicability.

Overall, DirectionNet serves as a promising direction for pose estimation tasks, setting a benchmark for accuracy and robustness in unstructured and difficult vision scenarios.