Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency

AAAI 2021

Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency

Feb 04, 2021
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Abstract: We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we highlight the fundamental difference between inverse and forward projection while modeling the individual motion of each rigid object, and propose a geometrically correct projection pipeline using a differentiable forward projection module. Second, we design a unified instance-aware photometric and geometric consistency loss that holistically imposes self-supervisory signals for every background and object region. Lastly, we introduce a general-purpose auto-annotation scheme using any off-the-shelf instance segmentation and optical flow models to produce video instance segmentation maps that will be utilized as input to our training pipeline. These proposed elements are validated in a detailed ablation study. Through extensive experiments conducted on the KITTI and Cityscapes dataset, our framework is shown to outperform the state-of-the-art depth and motion estimation methods. Auhtors: Seokju Lee, Sunghoon Im, Stephen Lin, In So Kweon (KAIST, Microsoft)

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