Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3Dobject detection architecture onpoint clouds.

Inspired by the neural mechanisms that may contribute to the brain's associative power, specifically the cortical modularization and hippocampal pattern completion, here we propose a self-supervised controllable generation (SCG) framework.

We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.

In real-world applications, event data is typically received in a streaming manner, where the distribution of patterns may shift over time. Additionally, privacy and memory constraints are commonly observed in practical scenarios, further compounding the challenges.

In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.

Our supplementary includes author statement, licensing, and implementation details of benchmark results for reproducibility. We bear all responsibilities for licensing, distributing, and maintaining our dataset. The proposed dataset is under the CC BY -NC-SA 4.0 license, while the code in the repository is For what purpose was the dataset created? The dataset comprises various types of annotations, including instances and topology relationships. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset?