4 accepted papers
Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of a network derived from the highly activated feature map. In this study, we first revealed that a norm of the feature map…
In this letter, we propose a method to model architectural components in large-scale indoor spaces from point cloud measurements. The proposed method enables the modeling of curved surfaces, cylindrical pillars, and slanted surfaces, which cannot be modeled using existing approaches. It operates by
In indoor environments, there exists a few distinctive indoor spaces' features (ISFs). However, up to our knowledge, there is no algorithm that fully utilizes ISF for accurate 3-D SLAM. In this letter, we suggest a sensor system that efficiently captures ISF and propose an algorithm framework that a
In this paper, a realtime pseudo-structure extraction algorithm for 3D indoor point cloud data (PCD) is proposed. This algorithm is called Convex Cut (CC) because of its two main steps: cutting the PCD with arbitrary planes, and extracting convex parts. CC can be used as a preprocessing module for o…