LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation (2406.19833v2)

Abstract: We present LightStereo, a cutting-edge stereo-matching network crafted to accelerate the matching process. Departing from conventional methodologies that rely on aggregating computationally intensive 4D costs, LightStereo adopts the 3D cost volume as a lightweight alternative. While similar approaches have been explored previously, our breakthrough lies in enhancing performance through a dedicated focus on the channel dimension of the 3D cost volume, where the distribution of matching costs is encapsulated. Our exhaustive exploration has yielded plenty of strategies to amplify the capacity of the pivotal dimension, ensuring both precision and efficiency. We compare the proposed LightStereo with existing state-of-the-art methods across various benchmarks, which demonstrate its superior performance in speed, accuracy, and resource utilization. LightStereo achieves a competitive EPE metric in the SceneFlow datasets while demanding a minimum of only 22 GFLOPs and 17 ms of runtime, and ranks 1st on KITTI 2015 among real-time models. Our comprehensive analysis reveals the effect of 2D cost aggregation for stereo matching, paving the way for real-world applications of efficient stereo systems. Code will be available at \url{https://github.com/XiandaGuo/OpenStereo}.

• The paper introduces a stereo-matching network that emphasizes channel-boosted 3D cost aggregation to achieve efficient and high-precision disparity estimation.
• It employs inverted residual blocks and a multi-scale convolutional attention module to balance computational cost (22 GFLOPs, 17 ms inference) with robust feature extraction.
• Experimental validations on SceneFlow and KITTI benchmarks demonstrate competitive accuracy (EPE of 0.51) and real-time performance in diverse environments.

Overview of LightStereo: Channel Boost for Efficient 2D Cost Aggregation

The paper introduces LightStereo, an innovative stereo-matching network engineered for enhanced computational efficiency. The primary objective of LightStereo is to streamline the stereo-matching process by focusing on 3D cost volumes as opposed to the traditional 4D approach, reducing computational demands while ensuring high accuracy.

Key Contributions

LightStereo's main contribution is its optimization strategy centered on the channel dimension of the 3D cost volume. By honing in on this dimension, the network can effectively manage the distribution of matching costs, achieving superior performance regarding both precision and efficiency. This approach underscores the importance of the disparity channel over spatial dimensions, a hypothesis supported by an exhaustive exploration of channel capacity amplification strategies.

Methodology

1. Inverted Residual Blocks for Cost Aggregation: The network leverages inverted residual blocks, which selectively enhance the disparity channel dimension. This choice is based on experimental findings indicating its advantage over alternative convolution types, including regular and depthwise separable convolutions. Inverted residuals are shown to provide a critical balance between computational efficiency and feature richness, crucial for real-time applications.
2. Multi-Scale Convolutional Attention (MSCA) Module: Inspired by advanced image segmentation techniques, MSCA optimizes cost aggregation by extracting essential image features via strip convolutions. This module is pivotal in leveraging image semantics to inform the matching process, effectively halting propagation when encountering disparity discontinuities.
3. Network Architecture: LightStereo's architecture combines multi-scale feature extraction with cost computation, aggregation, and disparity prediction. The network is designed to maintain low computational costs (22 GFLOPs) and rapid inference times (17 ms), placing it ahead of many existing state-of-the-art methods in terms of speed and resource utilization.

Experimental Validation

The experimental results on datasets such as SceneFlow and KITTI benchmarks demonstrate LightStereo's competitive edge over existing models. The network achieves an EPE metric of 0.51 on SceneFlow while maintaining low resource demands, highlighting its efficiency in both synthetic and real-world environments. LightStereo's variants further illustrate a potential trade-off between computational load and accuracy, allowing for flexibility based on specific application needs.

Implications and Future Directions

LightStereo's architecture holds significant implications for the deployment of stereo-matching networks in resource-constrained settings, such as autonomous vehicles and augmented reality systems. Its emphasis on channel-focused aggregation could pave the way for further reductions in computational complexity across related tasks in computer vision.

The findings suggest potential future developments in AI, particularly those that prioritize dimension-specific optimizations over traditional spatial expansions. Exploring additional adaptive channel boosting techniques and incorporating other state-of-the-art attention mechanisms could further augment LightStereo's applicability and efficiency.

In conclusion, LightStereo exemplifies a successful attempt at rethinking stereo-matching methodologies, with a particular focus on optimizing the disparity channel. This work not only presents a compelling alternative to current methods but also enriches our understanding of cost volume aggregation in three dimensions, making it a valuable reference for future research in the field.