Source-linked AI summary
Deep Homography Estimation for Dynamic Scenes
Hoang Le, Feng Liu, Shu Zhang, Aseem Agarwala
TL;DR
Deep homography estimation had not addressed dynamic content because existing training pairs were perfectly homography-alignable. The paper builds a dynamic-scene dataset and combines coarse-to-fine homography estimation with joint dynamics-mask prediction, reporting robust performance across challenging scenarios.
Problem
Existing deep homography methods focus on image pairs fully alignable by homographies, leaving their handling of dynamic scenes unstudied.
Method
The paper trains a multi-scale network on synthetic dynamic-scene examples and augments it to jointly estimate dynamics masks and homographies.
Results
The proposed networks handle dynamic scenes, blur, lack of texture, and poor lighting, while the dynamics-aware model exceeds 85% accuracy at ≤1.0 pixel on testing images.
Takeaways & Limitations
Dynamics-mask estimation enables the multi-scale homography network to deal with dynamic scenes better within the evaluated challenging scenarios.
Takeaways & Limitations
When trained on MS-COCO examples without dynamic scenes, the multi-scale network is less accurate on image pairs containing scene motion.
Abstract
from arXiv · showhide
Homography estimation is an important step in many computer vision problems. Recently, deep neural network methods have shown to be favorable for this problem when compared to traditional methods. However, these new methods do not consider dynamic content in input images. They train neural networks with only image pairs that can be perfectly aligned using homographies. This paper investigates and discusses how to design and train a deep neural network that handles dynamic scenes. We first collect a large video dataset with dynamic content. We then develop a multi-scale neural network and show that when properly trained using our new dataset, this neural network can already handle dynamic scenes to some extent. To estimate a homography of a dynamic scene in a more principled way, we need to identify the dynamic content. Since dynamic content detection and homography estimation are two tightly coupled tasks, we follow the multi-task learning principles and augment our multi-scale network such that it jointly estimates the dynamics masks and homographies. Our experiments show that our method can robustly estimate homography for challenging scenarios with dynamic scenes, blur artifacts, or lack of textures.
1. Introduction
Deep homography methods perform well on static or textureless image pairs but had not addressed dynamic scenes. This paper introduces dynamic-scene data, a multi-scale network, and joint dynamics-mask and homography estimation.
- Motivation: Dynamic scenes remain an understudied challenge because existing deep methods use image pairs fully alignable by homographies.Traditional approaches can reject dynamic objects as outliers with RANSAC, motivating comparable robustness in deep methods.
- Contributions: Its multi-scale network estimates homography at low resolution and progressively refines it at higher resolutions to handle large global motion.The architecture is introduced as a deep convolutional network designed for image pairs with large global motion.
- Contributions: The dynamics-aware network jointly estimates dynamics masks and homographies, using an auxiliary dynamics-mask loss alongside homography loss.The two tasks are treated as tightly coupled under a multi-task learning formulation.
- Contributions: The paper builds a large dynamic-scene video dataset and applies known homography sequences to static clips to generate training examples.The dataset contains 32,385 Creative Commons static video clips, and experiments report generalization to real-world videos.
- Results: Experiments report robustness to dynamic scenes, blurriness, lack of texture, and poor lighting conditions.The claimed robustness is the paper’s main reported outcome for the proposed method.
2. Related Work
Homography estimation methods trade off texture robustness against large-motion handling, while recent deep approaches combine local and global features but assume static scenes. The paper extends multi-stage deep estimation with coarse-to-fine processing and dynamics awareness.
- Classical Approaches: A homography relates corresponding points between images of planar scenes or images captured by a rotational camera.The relation is expressed as ˆx = Hx for homogeneous coordinates, and applies only to corresponding points on static objects.
- Classical Approaches: Direct methods tolerate textureless regions but struggle with large motion, whereas feature-based methods handle motion better but depend on reliable correspondences.Feature correspondences are often difficult to obtain in textureless or blurry images.
- Deep Approaches: Recent deep methods leverage local and global features and often outperform traditional methods on textureless images, but existing training focuses on static image pairs.Their behavior on dynamic scenes had not been studied.
- Paper’s Approach: The proposed multi-scale network starts at low resolution, progressively increases image size, and pre-aligns later stages using earlier homography estimates.These design choices target large motion and reduce global motion presented to later-stage networks.
- Paper’s Approach: The network additionally integrates a dynamics-mask component to handle dynamic scenes, which previous neural homography methods did not consider.This extension distinguishes the paper’s approach from earlier multi-stage methods.
3. Homography Dataset of Dynamic Scenes
Because standard warped-image datasets omit dynamic content, the paper constructs a dynamic-scene dataset from static-camera videos and known homography sequences. Conservative clip selection and synthetic pair generation aim to preserve homography alignment except for moving objects.
- Dataset Motivation: Existing datasets formed by warping individual MS-COCO images cannot provide training examples containing dynamic scenes.The paper therefore requires a different data-construction strategy.
- Dataset Construction: The dataset begins with videos captured using static cameras, then applies known homography sequences to generate image and video pairs.This construction supplies dynamic content together with known homographies.
- Clip Selection: Static clips are selected conservatively so image pairs can be perfectly aligned by a homography except for moving objects.The selection prioritizes high precision over high recall and includes manual examination of candidate clips.
- Dataset Statistics: The resulting collection contains 32,385 static video clips, averaging 22 frames, with scene motion ranging from 0–25 pixels.The dataset is split into 70% training, 20% testing, and 10% validation.
4. Homography Estimation Neural Networks
The paper develops a multi-scale homography network and augments it with dynamics-mask prediction so homography estimation can better handle dynamic scenes.
- 4.1. Multi-scale Neural Network: The multi-scale network estimates homographies from low-resolution image pairs and progressively refines them at higher resolutions to handle large motion.It cascades estimates across pyramid levels, pre-aligning inputs before subsequent stages.
- 4.1. Multi-scale Neural Network: Training on the dynamic dataset improves the multi-scale network’s ability to handle dynamic scenes, whereas MS-COCO training is limited to static-background pairs.The MS-COCO-trained model performs well when a homography can perfectly model the transformation, but is less accurate with scene motion.
- 4.2. Mask-augmented Deep Neural Network: The mask-augmented network adds a dynamics-mask estimator to the base network and jointly predicts dynamics maps and homography.Its outputs are propagated across scales through upsampling, warping, residual refinement, and concatenation.
- 4.2. Mask-augmented Deep Neural Network: The joint model is trained with homography and binary-cross-entropy mask losses, using optical-flow-derived masks as additional supervision.Training first supervises both outputs and later removes the mask-loss weight so predicted masks can improve homography estimation.
5. Experiments
Experiments evaluate multi-scale homography networks across static and dynamic datasets, showing strong static-scene performance and improved robustness to dynamic content through dynamic-scene training and mask augmentation.
- Evaluation on Static Scenes: On static scenes, MHN outperforms competitive deep learning methods and feature-matching or flow-based methods, although CLKN is slightly better in the high-precision region.The comparison uses MS-COCO and VidSets.
- Evaluation on Dynamic Scenes: More than 85% of dynamic-scene test images achieve mean corner error ≤1.0 pixel with the dynamics-mask-augmented network, versus around 80% for MHN.Both networks outperform existing learning-based and matching-based approaches on VidSetd.
- Experimental Setup: The experiments use MS-COCO, VidSets, and VidSetd, with mean corner error as the evaluation metric.VidSets contains static image pairs, whereas VidSetd contains dynamic-scene examples.
- Effect of Training Datasets: Training on VidSetd enables better dynamic-scene handling than training on its static counterpart, VidSets.The dataset comparison isolates the effect of dynamic-scene training examples.
- Discussions: Increasing the network from one to three scales accelerates training and enables larger global motion handling, while additional scales destabilize training.The instability is attributed to the very small image size processed at the first stage.
- Discussions: On optical-flow benchmark examples, the method aligns the background while treating foreground objects as outliers and identifies them in the dynamics map.This behavior is reported for Middlebury and Sintel examples.
6. Conclusion
The paper develops deep homography estimation for dynamic scenes using dynamic-scene data, multi-scale networks, and joint dynamics-mask and homography prediction. Experiments show improved handling of dynamic scenes alongside robustness to blur and limited texture.
- 6. Conclusion: The method combines a large dynamic-scene video dataset, a multi-scale network for large global motion, and joint dynamics-mask and homography estimation.The dynamics masks improve handling of dynamic scenes.
- 6. Conclusion: The networks achieve state-of-the-art homography estimation on examples derived from MS-COCO and handle challenging dynamic scenes, blurriness, and lack of texture.The conclusion reports these outcomes across the paper's experiments.