ABSTRACT
Dynamic Projection Mapping (DPM) necessitates geometric compensation of the projection image based on the position and orientation of moving objects. Additionally, the projector's shallow depth of field results in pronounced defocus blur even with minimal object movement. Achieving delay-free DPM with high image quality requires real-time implementation of geometric compensation and projector deblurring. To meet this demand, we propose a framework comprising two neural components: one for geometric compensation and another for projector deblurring. The former component warps the image by detecting the optical flow of each pixel in both the projection and captured images. The latter component performs real-time sharpening as needed. Ideally, our network's parameters should be trained on data acquired in an actual environment. However, training the network from scratch while executing DPM, which demands real-time image generation, is impractical. Therefore, the network must undergo pre-training. Unfortunately, there are no publicly available large real datasets for DPM due to the diverse image quality degradation patterns. To address this challenge, we propose a realistic synthetic data generation method that numerically models geometric distortion and defocus blur in real-world DPM. Through exhaustive experiments, we have confirmed that the model trained on the proposed dataset achieves projector deblurring in the presence of geometric distortions with a quality comparable to state-of-the-art methods.
ABSTRACT
This paper presents a shadowless projection mapping system for interactive applications in which a target surface is frequently occluded from a projector with a user's body. We propose a delay-free optical solution for this critical problem. Specifically, as the primary technical contribution, we apply a large format retrotransmissive plate to project images onto the target surface from wide viewing angles. We also tackle technical issues unique to the proposed shadowless principle. First, the retrotransmissive optics inevitably suffer from stray light, which leads to significant contrast degradation of the projected result. We propose to block the stray light by covering the retrotransmissive plate with a spatial mask. Because the mask reduces not only the stray light but the achievable luminance of the projected result, we develop a computational algorithm that determines the shape of the mask to balance the image quality. Second, we propose a touch sensing technique by leveraging the optically bidirectional property of the retrotransmissive plate to support interaction between the user and the projected contents on the target object. We implement a proof-of-concept prototype and validate the above-mentioned techniques through experiments.
ABSTRACT
Projector deblurring is an important technology for dynamic projection mapping (PM), where the distance between a projector and a projection surface changes in time. However, conventional projector deblurring techniques do not support dynamic PM because they need to project calibration patterns to estimate the amount of defocus blur each time the surface moves. We present a deep neural network that can compensate for defocus blur in dynamic PM. The primary contribution of this paper is a unique network structure that consists of an extractor and a generator. The extractor explicitly estimates a defocus blur map and a luminance attenuation map. These maps are then injected into the middle layers of the generator network that computes the compensation image. We also propose a pseudo-projection technique for synthesizing physically plausible training data, considering the geometric misregistration that potentially happens in actual PM systems. We conducted simulation and actual PM experiments and confirmed that: (1) the proposed network structure is more suitable than a simple, more general structure for projector deblurring; (2) the network trained with the proposed pseudo-projection technique can compensate projection images for defocus blur artifacts in dynamic PM; and (3) the network supports the translation speed of the surface movement within a certain range that covers normal human motions.
Subject(s)
Algorithms , Computer Graphics , Artifacts , Computer Simulation , Humans , Neural Networks, ComputerABSTRACT
Projection blur can occur in practical use cases that have non-planar and/or multi-projection display surfaces with various scattering characteristics because the surface often causes defocus and subsurface scattering. To address this issue, we propose ProDebNet, an end-to-end real-time projection deblurring network that synthesizes a projection image to minimize projection blur. The proposed method generates a projection image without explicitly estimating any geometry or scattering characteristics of the projection screen, which makes real-time processing possible. In addition, ProDebNet does not require real captured images for training data; we design a "pseudo-projected" synthetic dataset that is well-generalized to real-world blur data. Experimental results demonstrate that the proposed ProDebNet compensates for two dominant types of projection blur, i.e., defocus blur and subsurface blur, significantly faster than the baseline method, even in a real-projection scene.
