Boosting accuracy for efficient 2D stereo matching via attention-guided fusion and spatially adaptive modulation

As a dense pixel-wise matching task, high-accuracy stereo matching generally incurs substantial computational overhead, leading to high deployment costs and inapplicability to many practical scenarios with limited computing resources or stringent low-latency demands. This has spurred a growing shift toward lightweight stereo matching models, yet such models remain far from satisfactory in accuracy, creating a notable accuracy gap. To address the pressing need for lightweight and high-accuracy stereo matching models, we propose LASM-Net, a lightweight stereo matching network with superior disparity accuracy. First, we discard the mainstream strategy of aggregating 4D cost volumes with 3D convolutions, which is computationally expensive, and instead adopt multi-scale 3D cost volumes and 2D convolutions, which feature low computational overhead. To mitigate the consequent accuracy degradation, spatially adaptive modulation matrices are learned to modulate the 3D cost volume across all three dimensions, thereby fully exploiting the disparity distribution cues and the spatial contextual information. Second, to curb excessive computational costs, existing lightweight models have to significantly reduce the number of convolutional layers, which greatly restricts the size of receptive fields and thus leads to a lack of contextual information. To address this dilemma, a multi-scale attention-guided fusion strategy is proposed, which effectively compensates for the lost contextual information with the help of low-resolution cost volumes. These key ideas, along with other tailored designs, are integrated to form our LASM-Net, which demonstrates superior performance in terms of both accuracy and inference speed, thereby narrowing the accuracy gap between lightweight and accuracy-oriented models significantly.