Non-Line-of-Sight 3D Object Reconstruction via mmWave Surface Normal Estimation
Laura Dodds, Tara Boroushaki, Kaichen Zhou, Fadel Adib
Abstract
This paper presents the design, implementation, and evaluation of mmNorm, a new and highly-accurate method for non-line-of-sight 3D object reconstruction using millimeter wave (mmWave) signals. In contrast to past approaches for millimeter-wave-based imaging that perform backprojection for 3D object reconstruction, mmNorm reconstructs the surface by estimating the object's surface normals. To do this, it introduces a novel algorithm that directly estimates the surface normal vector field from mmWave reflections. By then inverting the normal field, it can reconstruct structural isosurfaces, then solve for the exact surface through a novel mmWave optimization framework.
Topics & Concepts
NormalSurface reconstructionComputer visionIterative reconstructionArtificial intelligenceSurface (topology)Object (grammar)Computer scienceExtremely high frequency3D reconstructionField (mathematics)Contrast (vision)Image (mathematics)Object detectionAlgorithmCognitive neuroscience of visual object recognitionPattern recognition (psychology)Optimization problemRemote sensingAdvanced Optical Sensing TechnologiesRobotics and Sensor-Based LocalizationRemote Sensing and LiDAR Applications