DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning

• URL: http://arxiv.org/abs/2506.14709v2
• Date: Thu, 31 Jul 2025 19:52:38 GMT
• Title: DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning
• Authors: Kunal Swami, Debtanu Gupta, Amrit Kumar Muduli, Chirag Jaiswal, Pankaj Kumar Bajpai,
• Abstract summary: DiFuse-Net is a novel decoupled network design for disentangled RGB and DP based depth estimation.<n>WBiPAM captures subtle DP disparity cues unique to smartphone cameras with small aperture.<n>A separate encoder extracts contextual information from the RGB image, and these features are fused to enhance depth prediction.
• Score: 1.456352735394398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth estimation is crucial for intelligent systems, enabling applications from autonomous navigation to augmented reality. While traditional stereo and active depth sensors have limitations in cost, power, and robustness, dual-pixel (DP) technology, ubiquitous in modern cameras, offers a compelling alternative. This paper introduces DiFuse-Net, a novel modality decoupled network design for disentangled RGB and DP based depth estimation. DiFuse-Net features a window bi-directional parallax attention mechanism (WBiPAM) specifically designed to capture the subtle DP disparity cues unique to smartphone cameras with small aperture. A separate encoder extracts contextual information from the RGB image, and these features are fused to enhance depth prediction. We also propose a Cross-modal Transfer Learning (CmTL) mechanism to utilize large-scale RGB-D datasets in the literature to cope with the limitations of obtaining large-scale RGB-DP-D dataset. Our evaluation and comparison of the proposed method demonstrates its superiority over the DP and stereo-based baseline methods. Additionally, we contribute a new, high-quality, real-world RGB-DP-D training dataset, named Dual-Camera Dual-Pixel (DCDP) dataset, created using our novel symmetric stereo camera hardware setup, stereo calibration and rectification protocol, and AI stereo disparity estimation method.

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