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Martin Gerdzhev

2 accepted papers

2021

LiDAR few-shot domain adaptation via integrated CycleGAN and 3D object detector with joint learning delay

ICRA 2021

he success of supervised LiDAR perception methods relies on the availability of large sets of labeled point cloud data, for which the labeling process is costly and time consuming. Given unpaired LiDAR datasets of similar sizes from two domains, with one (source) containing task-specific labels e.g.

Cited by 17SourceScholar
2021

TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module

ICRA 2021poster

Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving. In this paper, we introduce TORNADO-Net - a neural network for 3D LiDAR point cloud semantic segmentation. We incorporate a multi-view (bird-eye and range) projection feature extracti…

Cited by 101SourceScholar