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SegStereo: Exploiting Semantic Information for Disparity Estimation (1807.11699v1)

Published 31 Jul 2018 in cs.CV

Abstract: Disparity estimation for binocular stereo images finds a wide range of applications. Traditional algorithms may fail on featureless regions, which could be handled by high-level clues such as semantic segments. In this paper, we suggest that appropriate incorporation of semantic cues can greatly rectify prediction in commonly-used disparity estimation frameworks. Our method conducts semantic feature embedding and regularizes semantic cues as the loss term to improve learning disparity. Our unified model SegStereo employs semantic features from segmentation and introduces semantic softmax loss, which helps improve the prediction accuracy of disparity maps. The semantic cues work well in both unsupervised and supervised manners. SegStereo achieves state-of-the-art results on KITTI Stereo benchmark and produces decent prediction on both CityScapes and FlyingThings3D datasets.

Citations (331)

Summary

  • The paper introduces a dual-stream framework that embeds semantic features into disparity estimation to reduce errors in low-texture regions.
  • It integrates semantic feature embedding and semantic loss regularization to enhance performance on datasets like KITTI, CityScapes, and FlyingThings3D.
  • Experimental results show significant reductions in EPE and D1 errors, setting a new benchmark for stereo matching accuracy.

Exploiting Semantic Information for Disparity Estimation: A Detailed Analysis of SegStereo

The paper "SegStereo: Exploiting Semantic Information for Disparity Estimation" delineates an advanced method to improve disparity estimation by integrating semantic cues with traditional disparity estimation frameworks. Disparity estimation, a critical task in computer vision, involves determining the distance of objects by calculating the displacement between corresponding pixels in binocular stereo images. This research addresses the prevalent issue of accurately estimating disparities in featureless regions by innovating a new approach that utilizes high-level semantic information, enhancing both supervised and unsupervised disparity prediction.

Framework and Methodology

The proposed SegStereo model introduces a mechanism for embedding semantic features into a standard disparity estimation network, leveraging the ResNet architecture as a base. The correlation between stereo images is calculated using a cost volume, which is enriched with semantic features acquired from a segmentation network sharing parts of its architecture with the disparity network. This dual-functionality ensures enhanced feature encoding that combines both low and high-level representations, catering especially to areas in the image where traditional disparity estimation struggles due to lack of distinct features.

The integration of semantic cues is twofold:

  1. Semantic Feature Embedding: Semantic features derived from segmentation contribute object-level information directly into the disparity prediction framework. This improves the network's capability to handle regions with minimal texture and distinguish between foreground and background more effectively.
  2. Semantic Loss Regularization: A semantic softmax loss is incorporated into the learning process, providing additional supervision by enforcing semantic consistency between warped semantic features and ground-truth segmentation labels. This reinforcement, particularly beneficial in unsupervised settings, guides the model to refine its disparity predictions based on semantic class probabilities.

Experimental Validation

The SegStereo framework was validated across multiple datasets, including KITTI Stereo, CityScapes, and FlyingThings3D. In unsupervised settings, SegStereo demonstrated a significant reduction in disparity estimation errors when compared to models that did not utilize semantic guidance, illustrating the value of semantic feature embedding and loss regularization. The reduction in both EPE (End-Point Error) and D1 error on the KITTI dataset confirms the efficacy of integrating semantic information into disparity estimation.

In a supervised context, where ground truth disparities are available, SegStereo maintained state-of-the-art performance. The results underscore the model's ability to generalize effectively across different scenes and environmental contexts, affirming its robustness and versatility. Notably, the paper documents SegStereo's superior performance on the KITTI benchmark, marking a significant advancement in the field.

Implications and Future Directions

The integration of semantic information into disparity estimation networks heralds a promising direction for improving computer vision models, especially in tasks demanding high precision and reliability such as autonomous driving and robot navigation. The SegStereo model stands as a testament to the potential of multi-task learning frameworks that couple segmentation and disparity estimation for enhanced scene understanding.

Looking forward, the incorporation of additional cues, such as temporal coherence in video sequences or leveraging depth from monoscopic sequences, could further enrich the performance of disparity estimation frameworks. Furthermore, exploring the scalability of this approach to accommodate larger datasets and real-time processing requirements will be crucial for its deployment in practical applications.

In summation, the SegStereo model significantly advances the application of semantic information in disparity estimation, setting a new benchmark in the field. This paper contributes a well-defined, methodologically robust framework that integrates key data-driven insights to improve the accuracy and applicability of disparity prediction, paving the way for future research and development in smart vision systems.