fangchangma/sparse-to-dense
A Torch implementation of a deep neural network for predicting dense depth maps from sparse depth samples and a single RGB image.
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This repository implements the training and testing of deep regression neural networks for depth prediction from sparse depth samples combined with a single image input. The model supports RGB-based, sparse-depth-based, and RGBd-based depth prediction modes. It uses Torch with CUDA and cuDNN for GPU acceleration and was originally published at ICRA 2018.
Frequently asked
- What is fangchangma/sparse-to-dense?
- A Torch implementation of a deep neural network for predicting dense depth maps from sparse depth samples and a single RGB image.
- Is sparse-to-dense open source?
- Yes — fangchangma/sparse-to-dense is an open-source project tracked on heatdrop.
- What language is sparse-to-dense written in?
- fangchangma/sparse-to-dense is primarily written in Lua.
- How popular is sparse-to-dense?
- fangchangma/sparse-to-dense has 444 stars on GitHub.
- Where can I find sparse-to-dense?
- fangchangma/sparse-to-dense is on GitHub at https://github.com/fangchangma/sparse-to-dense.