Metric Relative Pose Estimation from 2D Images without Depth Supervision

Core Concepts

MicKey, a neural network, can predict metric 3D keypoint coordinates in camera space from 2D input images, enabling metric relative pose estimation between image pairs without requiring depth measurements or scene reconstructions.

Abstract

The content presents MicKey, a neural network that can predict metric 3D keypoint coordinates and descriptors from a single 2D input image. By establishing 3D-3D correspondences via descriptor matching, MicKey can then recover the metric relative pose between two images without requiring any depth measurements or scene reconstructions.
Key highlights:

MicKey addresses the scale ambiguity problem in relative pose estimation by learning to predict 3D keypoint coordinates in camera space.
MicKey is trained in an end-to-end fashion using only relative pose supervision, without requiring depth measurements or knowledge about image pair overlap.
MicKey outperforms state-of-the-art methods on the Map-free Relocalisation benchmark, which evaluates the suitability of relative pose estimates for augmented reality applications.
MicKey's depth predictions are tailored towards the feature matching task, improving the performance of top-ranking matchers when used in combination.
MicKey exhibits the ability to reason about the 3D structure of the scene, establishing correspondences even in cases of extreme viewpoint changes where other matchers fail.

Stats

"Given two images, we can estimate the relative camera pose between them by establishing image-to-image correspondences."
"Estimating the relative pose between two images is a long-standing problem in computer vision."
"The general setting, as recently formalized as "Map-free Relocalisation", provides only two images and intrinsics but no further measurements."

Quotes

"Whether you prefer inches or centimeters, we measure and understand the world in scale-metric units. Unfortunately, the scale-metric quality of the world is lost when we project it to the image plane."
"The general setting, as recently formalized as "Map-free Relocalisation", provides only two images and intrinsics but no further measurements."
"Contrary to previous keypoint extractors, MicKey requires learning the 3D coordinates of keypoints."

Key Insights Distilled From

Matching 2D Images in 3D

by Axel Barroso... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06337.pdf

Deeper Inquiries

How could MicKey's architecture be extended to handle higher-resolution feature maps without compromising its current performance

To handle higher-resolution feature maps without compromising performance, MicKey's architecture could be extended by incorporating techniques such as multi-scale processing and feature pyramid networks. By introducing additional branches in the network that operate at different scales, MicKey can capture both fine-grained details and global context. This approach would involve feeding the input image through multiple convolutional layers with varying receptive fields to extract features at different levels of abstraction. Additionally, the use of skip connections can help preserve spatial information and enable the network to combine features from different scales effectively. By integrating these strategies, MicKey can adapt to higher-resolution feature maps while maintaining its ability to reason about 3D structure from 2D images.

What other applications beyond relative pose estimation could benefit from MicKey's ability to reason about 3D structure from 2D images

Beyond relative pose estimation, MicKey's capability to infer 3D structure from 2D images can benefit various applications in computer vision. One such application is object detection and localization in images. By leveraging MicKey's ability to establish metric correspondences in 3D space, object detection systems can improve their accuracy by incorporating depth information into the detection process. This can help in accurately localizing objects in 3D space, especially in scenarios where traditional 2D detection methods may struggle with occlusions or cluttered backgrounds. Additionally, MicKey's approach can be applied to tasks like image registration, scene understanding, and even robotics for navigation and mapping tasks.

How could the insights from MicKey's training strategy, which learns to deal with non-overlapping image pairs, be applied to improve other computer vision tasks that rely on establishing correspondences across views

The insights from MicKey's training strategy, which learns to deal with non-overlapping image pairs, can be applied to enhance various computer vision tasks that rely on establishing correspondences across views. For instance, in image stitching and panorama generation, where matching features across images is crucial, MicKey's approach can improve the robustness and accuracy of feature matching algorithms. By training on non-overlapping image pairs, the network can learn to generalize better to challenging scenarios with limited visual overlap. Similarly, in visual SLAM (Simultaneous Localization and Mapping), MicKey's training strategy can help improve the reliability of feature matching and pose estimation in dynamic environments or scenes with significant changes between frames. By adapting this training strategy to other tasks, computer vision systems can become more resilient to variations in image content and viewpoint changes.