Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Towards Equilibrium

Core Concepts

The authors propose a self-supervised depth and pose estimation model, DualRefine, that tightly couples depth and pose estimation through a feedback loop. The model iteratively refines depth estimates and a hidden state of feature maps by computing local matching costs based on epipolar geometry, and uses the refined depth estimates and feature maps to compute pose updates at each step.

Abstract

The authors propose the DualRefine model, which tightly couples depth and pose estimation through a feedback loop. The key components are:

Iterative update module:

Samples candidate matches along the epipolar line that evolves based on the current pose estimates
Uses the sampled matching costs to infer per-pixel confidences that are used to compute depth refinements
Updates the depth estimates are then used in direct feature-metric alignments to refine the pose updates towards convergence

Deep equilibrium (DEQ) framework:

Allows the depth and pose updates to reach a fixed point through iterative refinement
Enables memory-efficient training by not requiring saving gradients for operations prior to the fixed point

Experiments:

Achieves competitive depth prediction and odometry prediction performance on the KITTI dataset, surpassing published self-supervised baselines
Demonstrates improved global consistency of visual odometry results compared to other learning-based models

The authors show that their approach of tightly coupling depth and pose estimation, and iteratively refining them using epipolar geometry and direct alignments, leads to improved performance in both tasks compared to prior self-supervised methods.

Stats

The authors report the following key metrics on the KITTI dataset:
Depth estimation:

Absolute Relative Error (Abs Rel): 0.087
Squared Relative Error (Sq Rel): 0.698
Root Mean Squared Error (RMSE): 4.234
Accuracy under threshold δ1: 0.914
Visual odometry:

Translation error (terr) on Seq 09: 3.43%
Rotation error (rerr) on Seq 09: 1.04°/100m
Absolute Trajectory Error (ATE) on Seq 09: 5.18m
Translation error (terr) on Seq 10: 6.80%
Rotation error (rerr) on Seq 10: 1.13°/100m
Absolute Trajectory Error (ATE) on Seq 10: 10.85m

Quotes

None

Key Insights Distilled From

DualRefine

by Antyanta Ban... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2304.03560.pdf

Deeper Inquiries

How could the proposed iterative refinement approach be extended to handle dynamic objects and occlusions more effectively

To handle dynamic objects and occlusions more effectively, the iterative refinement approach could be extended in several ways. One approach could involve incorporating motion priors or optical flow information to better predict the movement of dynamic objects and adjust the depth estimates accordingly. By integrating temporal information and motion cues, the model can adapt to changes in the scene and improve the accuracy of depth predictions in the presence of dynamic elements. Additionally, the model could utilize semantic segmentation information to identify and mask out dynamic objects during the refinement process, focusing the updates on more stable scene elements. By dynamically adjusting the refinement process based on the presence of dynamic objects and occlusions, the model can enhance its ability to handle challenging scenarios.

What other geometric constraints or multi-view reasoning could be incorporated to further improve the accuracy and robustness of the depth and pose estimates

To further improve the accuracy and robustness of the depth and pose estimates, additional geometric constraints and multi-view reasoning could be incorporated into the framework. One approach could involve leveraging epipolar geometry more extensively to guide the refinement process. By enforcing geometric consistency across multiple views and frames, the model can enhance the accuracy of depth and pose estimates. Furthermore, integrating scene priors, such as known object sizes or scene layout constraints, can provide valuable information to refine the estimates. By combining geometric constraints, multi-view reasoning, and scene priors, the model can achieve more reliable depth and pose predictions in complex environments.

Given the tight coupling between depth and pose, how could this framework be adapted to enable joint optimization of other related tasks, such as semantic segmentation or object detection

The framework's tight coupling between depth and pose estimation opens up opportunities for joint optimization of other related tasks, such as semantic segmentation or object detection. By integrating semantic segmentation information into the refinement process, the model can leverage object boundaries and semantic context to improve depth and pose estimates. Additionally, incorporating object detection outputs can help refine the estimates based on detected objects in the scene, enhancing the overall scene understanding. By jointly optimizing depth, pose, semantic segmentation, and object detection tasks within the same framework, the model can achieve a more comprehensive understanding of the scene and improve the accuracy of each individual task through mutual reinforcement.