Ruho Kondo — accepted AI-conference papers

2021

PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation

Noriaki Hirose, Satoshi Koide, Keisuke Kawano, Ruho Kondo

ICRA 2021poster

We propose a novel objective for penalizing geometric inconsistencies and improving the depth and pose estimation performance of monocular camera images. Our objective is designed using the Wasserstein distance between two point clouds, estimated from images with different camera poses. The Wasserst…

Cited by 7 Source ↗Scholar ↗

2019

Flow-based Image-to-Image Translation with Feature Disentanglement

Ruho Kondo, Keisuke Kawano, Satoshi Koide, Takuro Kutsuna

NeurIPS 2019poster

Learning non-deterministic dynamics and intrinsic factors from images obtained through physical experiments is at the intersection of machine learning and material science. Disentangling the origins of uncertainties involved in microstructure growth, for example, is of great interest because future…

Cited by 15 Source ↗PDF ↗Scholar ↗