5 citations · 8 across the 10 of their papers we have counts for
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cs.RO2026
FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity
Ganghyeon Lee, Inha Lee, Junhee Lee +3
Although recent robot perception research emphasizes training on data from diverse environments to improve generalization, most existing methods still rely on centralized learning,…
cs.RO2024★ 5 cited
A Benchmark Dataset for Collaborative SLAM in Service Environments
Harin Park, Inha Lee, Minje Kim +2
As service environments have become diverse, they have started to demand complicated tasks that are difficult for a single robot to complete. This change has led to an interest in…
cs.RO2024★ 3 cited
AiSDF: Structure-aware Neural Signed Distance Fields in Indoor Scenes
Jaehoon Jang, Inha Lee, Minje Kim +1
Indoor scenes we are living in are visually homogenous or textureless, while they inherently have structural forms and provide enough structural priors for 3D scene reconstruction.…