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Found 5 papers, 4 papers with code
Probabilistic Implicit Scene Completion
We also demonstrate that our approach outperforms deterministic models even in less ambiguous cases with a small amount of missing data, which infers that probabilistic formulation is crucial for high-quality geometry completion on input scans exhibiting any levels of completeness.
N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras
We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object recognition with event cameras.
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on N-ImageNet (mini)
Learning to Generate 3D Shapes with Generative Cellular Automata
We formulate the shape generation process as sampling from the transition kernel of a Markov chain, where the sampling chain eventually evolves to the full shape of the learned distribution.
Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning.
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training.
