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3Dセグメンテーション/視覚障害支援arXiv:2107.03180

HIDA:ウェアラブル固体LiDARと3Dインスタンスセグメンテーションによる視覚障害者向け屋内理解支援システム

HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor

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固体LiDARで取得した3D点群を軽量なインスタンスセグメンテーションモデルで解析し、障害物回避と物体探索を音声案内する視覚障害者向け支援システムを提案・実証した。

著者: Huayao Liu, Ruiping Liu, Kailun Yang, Jiaming Zhang, Kunyu Peng, Rainer Stiefelhagen

分類: cs.CV, cs.HC, cs.RO

原文アブストラクト

Independently exploring unknown spaces or finding objects in an indoor environment is a daily but challenging task for visually impaired people. However, common 2D assistive systems lack depth relationships between various objects, resulting in difficulty to obtain accurate spatial layout and relative positions of objects. To tackle these issues, we propose HIDA, a lightweight assistive system based on 3D point cloud instance segmentation with a solid-state LiDAR sensor, for holistic indoor detection and avoidance. Our entire system consists of three hardware components, two interactive functions~(obstacle avoidance and object finding) and a voice user interface. Based on voice guidance, the point cloud from the most recent state of the changing indoor environment is captured through an on-site scanning performed by the user. In addition, we design a point cloud segmentation model with dual lightweight decoders for semantic and offset predictions, which satisfies the efficiency of the whole system. After the 3D instance segmentation, we post-process the segmented point cloud by removing outliers and projecting all points onto a top-view 2D map representation. The system integrates the information above and interacts with users intuitively by acoustic feedback. The proposed 3D instance segmentation model has achieved state-of-the-art performance on ScanNet v2 dataset. Comprehensive field tests with various tasks in a user study verify the usability and effectiveness of our system for assisting visually impaired people in holistic indoor understanding, obstacle avoidance and object search.

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