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Independently exploring unknown spaces or finding objects in an indoorenvironment is a daily but challenging task for visually impaired people.However, common 2D assistive systems lack depth relationships between variousobjects, resulting in difficulty to obtain accurate spatial layout and relativepositions of objects. To tackle these issues, we propose HIDA, a lightweightassistive system based on 3D point cloud instance segmentation with asolid-state LiDAR sensor, for holistic indoor detection and avoidance. Ourentire system consists of three hardware components, two interactivefunctions~(obstacle avoidance and object finding) and a voice user interface.Based on voice guidance, the point cloud from the most recent state of thechanging indoor environment is captured through an on-site scanning performedby the user. In addition, we design a point cloud segmentation model with duallightweight decoders for semantic and offset predictions, which satisfies theefficiency of the whole system. After the 3D instance segmentation, wepost-process the segmented point cloud by removing outliers and projecting allpoints onto a top-view 2D map representation. The system integrates theinformation above and interacts with users intuitively by acoustic feedback.The proposed 3D instance segmentation model has achieved state-of-the-artperformance on ScanNet v2 dataset. Comprehensive field tests with various tasksin a user study verify the usability and effectiveness of our system forassisting visually impaired people in holistic indoor understanding, obstacleavoidance and object search.

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