Yoshikatsu Nakajima — accepted AI-conference papers

2019

Incremental Class Discovery for Semantic Segmentation With RGBD Sensing

Yoshikatsu Nakajima, Byeongkeun Kang, Hideo Saito, Kris Kitani

ICCV 2019poster

This work addresses the task of open world semantic segmentation using RGBD sensing to discover new semantic classes over time. Although there are many types of objects in the real-word, current semantic segmentation methods make a closed world assumption and are trained only to segment a limited nu…

Cited by 23 PDF ↗Scholar ↗

2018

Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation

Yoshikatsu Nakajima, Keisuke Tateno, Federico Tombari, Hideo Saito

IROS 2018poster

We propose an efficient and scalable method for incrementally building a dense, semantically annotated 3D map in real-time. The proposed method assigns class probabilities to each region, not each element (e.g., surfel and voxel), of the 3D map which is built up through a robust SLAM framework and i…

Cited by 51 Source ↗Scholar ↗