DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions

arXiv:2403.12202v1 Announce Type: new
Abstract: In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically, we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the bottleneck and skip connections. This effectively improves the performance of this simple network and sets it on par with the latest, complex transformer-based models. Leveraging the initial depths and features from …

abstract arxiv attention cs.cv features iterative novel paper spatial type