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Found 6 papers, 1 papers with code
OutCast: Outdoor Single-image Relighting with Cast Shadows
We propose a relighting method for outdoor images.
Curiosity-driven 3D Object Detection Without Labels
This can be overcome by a novel form of training, where an additional network is employed to steer the optimization itself to explore the entire parameter space i. e., to be curious, and hence, to resolve those ambiguities and find workable minima.
Finding Your (3D) Center: 3D Object Detection Using a Learned Loss
As we assume the scene not to be labeled by centers, no classic loss, such as Chamfer can be used to train it.
SynthCity: A large scale synthetic point cloud
With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images.
A review on deep learning techniques for 3D sensed data classification
In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data.
Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance
Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds.
