Papers › Adversarial learning for unguided single depth map completion of indoor scenes
Adversarial learning for unguided single depth map completion of indoor scenes
Moushumi Medhi, Rajiv Ranjan Sahay
Depth map completion without guidance from color images is a challenging, ill-posed problem. Conventional methods rely on computationally intensive optimization processes. This work proposes a deep adversarial learning approach to estimate missing depth information directly from a single degraded observation, without requiring RGB guidance or postprocessing.
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