In this work, we present Point Transformer, a deep neural network that operates directly on
unordered and unstructured point sets. We design Point Transformer to extract local and …

We address the problem of vehicle selflocalization from multi-modal sensor information and
a reference map. The map is generated off-line by extracting landmarks from the vehicle's …

Most of the existing research on value creation drivers in private equity buyouts is restricted
to the US and the UK In addition, most of the generally applied methods for measuring the …

Using a unique proprietary data set of 1980 realized and unrealized buyouts completed
between 1986 and 2010, we examine entry and exit pricing in buyouts and its influence on …

This work addresses the problem of point cloud registration using deep neural networks. We
propose an approach to predict the alignment between two point clouds with overlapping …

The comprehensive representation and understanding of the driving environment is crucial
to improve the safety and reliability of autonomous vehicles. In this paper, we present a new …

Using a unique proprietary data set of over 5400 realized and unrealized venture capital
investments between 1980 and 2005, we examine the impact of demand-related factors, eg …

We present a vehicle self-localization method using point-based deep neural networks. Our
approach processes measurements and point features, ie landmarks, from a high-definition …

In this paper we investigate the effects of debt usage on (risk-adjusted) equity returns in
private equity-backed buyout transactions. We use a proprietary data set of more than 700 …

Using a unique proprietary data set of 460 realized buyouts completed between 1990 and
2005, we examine the risk appetite of private equity (PE) sponsors in different states of the PE …