The paper presents SPIdepth, a novel self-supervised approach for monocular depth estimation that prioritizes the refinement of the pose network to enhance depth prediction accuracy.
Key highlights:
The paper first provides an overview of supervised and self-supervised depth estimation approaches, highlighting the potential of leveraging pose information. It then introduces the SPIdepth methodology, which comprises two primary components: DepthNet for depth prediction and PoseNet for relative pose estimation.
The authors explain how SPIdepth utilizes a state-of-the-art ConvNext as the pretrained encoder for DepthNet to capture detailed scene structures, and how it employs a powerful pretrained model for PoseNet to enhance the capture of complex scene structures and geometric relationships.
The training process involves simultaneously optimizing DepthNet and PoseNet by minimizing the photometric reprojection error, with additional regularization techniques to handle stationary cameras and dynamic objects.
The results section showcases SPIdepth's exceptional performance on the KITTI and Cityscapes datasets, outperforming previous self-supervised methods and even surpassing supervised models in certain metrics. The authors emphasize SPIdepth's ability to achieve state-of-the-art results using only a single image for inference, underscoring its efficiency and practicality.
Overall, the paper presents a significant advancement in the field of self-supervised monocular depth estimation, highlighting the importance of strengthening pose information for improving scene understanding and depth prediction accuracy.
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by Mykola Lavre... às arxiv.org 04-22-2024
https://arxiv.org/pdf/2404.12501.pdfPerguntas Mais Profundas