  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.
