58 citations · 64 across the 5 of their papers we have counts for
8 papers
AGO: Adaptive Grounding for Open World 3D Occupancy Prediction
Peizheng Li, Shuxiao Ding, You Zhou +6
Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects. Transferring open-vo…
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…
A Survey on Deep Domain Adaptation for LiDAR Perception
Larissa T. Triess, Mariella Dreissig, Christoph B. Rist +1
Scalable systems for automated driving have to reliably cope with an open-world setting. This means, the perception systems are exposed to drastic domain shifts, like changes in we…