Self-Calibrating Dynamic Projection Mapping System for Dynamic, Deformable Surfaces with Jitter Correction and Occlusion Handling

• Main Authors: Ibrahim, Muhammad Twaha, Gopi, M., Majumder, Aditi
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial Intelligence; Cameras; Computer Vision; Computing Methodologies; Data visualization; Image and Video Acquisition; Jitter; Manuals; Predictive models; Shape; Three-dimensional displays
• ISSN: 2473-0726
• DOI: 10.1109/ISMAR59233.2023.00044

Dynamic projection mapping (DPM) is becoming increasingly popular, enabling viewers to visualize information on moving and deformable surfaces. Examples include large data visualization on the moving walls of tents deployed in austere remote locations during emergency management or defense operations. A DPM system typically comprises a RGB-D camera and a projector. In this paper, we present the first fully functional DPM system that auto-calibrates (without any physical props like planar checkerboard or rigid 3D objects) and creates a comprehensible display in the presence of large and fast movements by managing jitter and occlusion by passing objects.Prior DPM systems need specific calibration props, manual inputs and in order to deliver sub-pixel calibration accuracy. Recalibration in the face of movement or change in system setup becomes a time consuming process where the calibration prop needs to be brought back. When rendering content using DPM, errors in calibration are exacerbated and the noise in the depth camera leads to jitter, making the projection unreadable or incomprehensible. Occlusion may disrupt operations completely by jumbling up even the unoccluded parts of the display.In this paper we propose key hardware-agnostic methods for DPM calibration and rendering to make DPM systems easily deployable, stable and legible. First, we present a novel projector-camera calibration that does not need synchronization of the devices and leverages the moving surface itself, a counter-intuitive proposition. We project ArUCo markers on the moving surface and use corresponding detected features of these markers in the RGB and depth camera over multiple frames to accurately estimate the intrinsics and extrinsics of both the projector and the RGB-D camera. Second, we present a DPM rendering method that uses Kalman filtering models to reduce jitter and predict the surface shape in the presence of short term occlusions by other static objects. This results in the first DPM system, to the best of our knowledge, that can auto-calibrate in minutes and can render high resolution content like high-resolution text or images comprehensible even in the presence of fast movements, deformations and occlusions. We compare and evaluate the accuracy with prior methods and analyze the effect of surface movement on the calibration accuracy.