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On Evolving Attention Towards Domain Adaptation
Metrics concerned:Accuracy
We formulate monocular depth estimation using denoising diffusion models,
inspired by their recent successes in high fidelity image generation. To that
end, we introduce innovations to address problems arising due to noisy,
incomplete depth maps in training data, including step-unrolled denoising
diffusion, an $L_1$ loss, and depth infilling during training. To cope with the
limited availability of data for supervised training, we leverage pre-training
on self-supervised image-to-image translation tasks. Despite the simplicity of
the approach, with a generic loss and architecture, our DepthGen model achieves
SOTA performance on the indoor NYU dataset, and near SOTA results on the
outdoor KITTI dataset. Further, with a multimodal posterior, DepthGen naturally
represents depth ambiguity (e.g., from transparent surfaces), and its zero-shot
performance combined with depth imputation, enable a simple but effective
text-to-3D pipeline. Project page: https://depth-gen.github.io
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