CCNeXt用新注意力机制实现快速高精度立体深度估计
CCNeXt: An Effective Self-Supervised Stereo Depth Estimation Approach
- 用窗口化对极交叉注意力提升特征匹配精度
- 在KITTI数据集上速度比当前最优模型快10.18倍
- 适合资源受限场景的实时深度感知应用
深度估计在机器人、自动驾驶和增强现实等应用中至关重要，但可靠真值数据难以获取。本文提出一种新型自监督立体深度估计方法CCNeXt，采用现代卷积网络特征提取器与创新的窗口化对极交叉注意力模块，结合重新设计的解码器结构。实验表明，该方法在KITTI Eigen Split测试集上表现优异，且推理速度比当前最佳模型快10.18倍；在改进版真实值和Driving Stereo数据集上所有指标均达领先水平。代码已开源，确保可复现性。
原文摘要 · Abstract (English)
Depth Estimation plays a crucial role in recent applications in robotics, autonomous vehicles, and augmented reality. These scenarios commonly operate under constraints imposed by computational power. Stereo image pairs offer an effective solution for depth estimation since it only needs to estimate the disparity of pixels in image pairs to determine the depth in a known rectified system. Due to the difficulty in acquiring reliable ground-truth depth data across diverse scenarios, self-supervised techniques emerge as a solution, particularly when large unlabeled datasets are available. We propose a novel self-supervised convolutional approach that outperforms existing state-of-the-art Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) while balancing computational cost. The proposed CCNeXt architecture employs a modern CNN feature extractor with a novel windowed epipolar cross-attention module in the encoder, complemented by a comprehensive redesign of the depth estimation decoder. Our experiments demonstrate that CCNeXt achieves competitive metrics on the KITTI Eigen Split test data while being 10.18$\times$ faster than the current best model and achieves state-of-the-art results in all metrics in the KITTI Eigen Split Improved Ground Truth and Driving Stereo datasets when compared to recently proposed techniques. To ensure complete reproducibility, our project is accessible at \href{https://github.com/alelopes/CCNext}{\texttt{https://github.com/alelopes/CCNext}}.
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