CNN-based synthesis of realistic high-resolution LiDAR data

Larissa T. Triess; David Peter; Christoph B. Rist; Markus Enzweiler; J. Marius Zöllner

doi:10.1109/ivs.2019.8813771

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arXiv · 1907.00787

CNN-based synthesis of realistic high-resolution LiDAR data

Larissa T. Triess·David Peter·Christoph B. Rist·Markus Enzweiler·J. Marius Zöllner

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Abstract

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 guidance from specialized loss-functions. First, we utilize a modified per-point loss that addresses missing LiDAR point measurements. Second, we align the quality of our generated output with real-world sensor data by applying a perceptual loss. In large-scale experiments on real-world datasets, we evaluate both the geometric accuracy and semantic segmentation performance using our generated data vs. ground truth. In a mean opinion score testing we further assess the perceptual quality of our generated point clouds. Our results demonstrate a significant quantitative and qualitative improvement in both geometry and semantics over traditional non CNN-based up-sampling methods.

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Larissa T. Triess, David Peter, Christoph B. Rist, Markus Enzweiler, J. Marius Zöllner. 2019-06-28. CNN-based synthesis of realistic high-resolution LiDAR data. https://doi.org/10.1109/ivs.2019.8813771

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