33 citations · 56 across the 7 of their papers we have counts for
7 papers
A Realism Metric for Generated LiDAR Point Clouds
Larissa T. Triess, Christoph B. Rist, David Peter +1
A considerable amount of research is concerned with the generation of realistic sensor data. LiDAR point clouds are generated by complex simulations or learned generative models. T…
Point Cloud Generation with Continuous Conditioning
Larissa T. Triess, Andre Bühler, David Peter +2
Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper…
Semi-Local Convolutions for LiDAR Scan Processing
Larissa T. Triess, David Peter, J. Marius Zöllner
A number of applications, such as mobile robots or automated vehicles, use LiDAR sensors to obtain detailed information about their three-dimensional surroundings. Many methods use…
Quantifying point cloud realism through adversarially learned latent representations
Larissa T. Triess, David Peter, Stefan A. Baur +1
Judging the quality of samples synthesized by generative models can be tedious and time consuming, especially for complex data structures, such as point clouds. This paper presents…
Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study
Larissa T. Triess, David Peter, Christoph B. Rist +1
Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment. State of the art methods use deep n…
CNN-based synthesis of realistic high-resolution LiDAR data
Larissa T. Triess, David Peter, Christoph B. Rist +2
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data. Our approach generates semantically and perceptually realistic results with…