Robust Equivariant Multiview Structure from Motion: Handling Outliers in Large-Scale 3D Reconstruction

Core Concepts

A deep learning-based approach for simultaneous recovery of camera poses and 3D scene structure from large image collections, which can effectively handle outliers in the input point tracks.

Abstract

The paper proposes a robust deep learning-based method for Multiview Structure from Motion (SfM) that can handle large image collections and outlier-contaminated point tracks. The key contributions are: An architecture that extends the recent equivariant SfM method of Moran et al. [23] by adding an inlier/outlier classification module that respects the equivariant structure of the network. This allows the method to identify and remove outlier track points. A robust bundle adjustment scheme that can handle classification errors in the inlier/outlier predictions. Experiments on the MegaDepth and 1DSFM datasets demonstrate that the proposed method can achieve state-of-the-art accuracy on large-scale SfM tasks, while considerably reducing runtime compared to the classical COLMAP method. The method first encodes the input point tracks using an equivariant feature encoder. It then classifies each track point as an inlier or outlier using an equivariant inlier/outlier classification module. The network is then fine-tuned on the filtered point tracks, and a final robust bundle adjustment step is applied to refine the camera poses and 3D structure.

Stats

The method is evaluated on the MegaDepth and 1DSFM datasets, which contain large-scale outdoor scenes reconstructed from community photo collections. The point tracks used as input are constructed by concatenating pairwise SIFT matches with RANSAC filtering, resulting in point tracks contaminated with outliers.

Quotes

"Existing heuristics for point track extraction, however, often return erroneous (outlier) point tracks, due to large viewpoint and illumination differences and the presence of repetitive scene structures." "Identifying these outlier track points significantly slows reconstruction down and in certain cases results in inaccurate reconstruction. Developing robust techniques that can efficiently and accurately remove outlier track points is still an outstanding challenge."

Key Insights Distilled From

RESFM: Robust Equivariant Multiview Structure from Motion

by Fadi Khatib,... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14280.pdf

Deeper Inquiries

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

To extend the proposed method to handle dynamic scenes or scenes with moving objects, several modifications and additions can be made to the existing architecture. One approach could involve incorporating motion estimation techniques to track and account for the movement of objects within the scene. This could involve integrating optical flow algorithms or deep learning-based motion estimation models to predict the trajectory of moving objects and adjust the camera poses and 3D structure accordingly. Additionally, the network could be trained on datasets specifically designed to simulate dynamic scenes, where objects move or change positions between frames. By including temporal information and dynamic object tracking capabilities, the network can adapt to changing scenes and improve the accuracy of camera pose and structure estimation in dynamic environments.

What are the limitations of the current inlier/outlier classification module, and how could it be improved to handle more challenging cases

The current inlier/outlier classification module has certain limitations that could be addressed to handle more challenging cases. One limitation is its sensitivity to noise and outliers in the input data, which can lead to misclassification of inliers and outliers. To improve the module, techniques such as robust loss functions or outlier-resistant training strategies could be implemented to make the classification more resilient to noisy data. Additionally, incorporating contextual information or spatial constraints into the classification process could help improve the accuracy of outlier detection. By leveraging spatial relationships between points and considering the overall structure of the scene, the classification module can better differentiate between inliers and outliers in complex scenarios.

Could the equivariant architecture be further leveraged to incorporate additional priors or constraints, such as scene geometry or semantic information, to improve the 3D reconstruction quality

The equivariant architecture can be further leveraged to incorporate additional priors or constraints to enhance the quality of 3D reconstruction. One way to achieve this is by integrating geometric constraints, such as smoothness priors or geometric consistency assumptions, into the network architecture. By enforcing geometric constraints during the reconstruction process, the network can produce more accurate and physically plausible 3D reconstructions. Moreover, semantic information about the scene, such as object categories or scene semantics, can be integrated into the network to improve the understanding of the scene structure. By incorporating semantic priors, the network can leverage high-level scene information to guide the reconstruction process and enhance the overall quality of the 3D reconstruction results.