Eduardo R. Corral-Soto — accepted AI-conference papers

2022

Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters

Mrigank Rochan, Shubhra Aich, Eduardo R. Corral-Soto, Amir Nabatchian, Bingbing Liu

ICRA 2022poster

In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance of an existing segmentation model when training (source domain) and testing (target domain) data originate from different…

Cited by 32 Source ↗Scholar ↗

2021

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

Eduardo R. Corral-Soto, Amir Nabatchian, Martin Gerdzhev, Bingbing Liu

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 17 Source ↗Scholar ↗