Autonomous driving paper index
Vision Transformer-Based Monocular Depth Estimation for Fisheye Cameras
One-line summary
In this paper, we propose the MonoFVT framework, which incorporates a vision transformer (ViT) into the traditional monocular self-supervised depth estimation network, enabling it to better handle the global distortions inherent in fisheye camera images.
Engineering notes
Key topics: autonomous driving, depth estimation, monocular depth, vision transformer, deployment. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Monocular self-supervised depth estimation is a critical perceptual challenge in autonomous driving. With the widespread adoption of fisheye cameras in this domain, adapting self-supervised depth estimation networks to fisheye camera inputs becomes both a challenging and practical task. In this paper, we propose the MonoFVT framework, which incorporates a vision transformer (ViT) into the traditional monocular self-supervised depth estimation network, enabling it to better handle the global distortions inherent in fisheye camera images. Our novel fisheye reprojection module is versatile, compatible with various fisheye camera models, and can be easily extended to different deployment scenarios. Additionally, we introduce a scheme that utilizes speed sensor data to help the network learn absolute depth.
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