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A new passive approach called Generalized Scene Reconstruction (GSR) enables generalized scenes to be effectively reconstructed. Generalized scenes are defined to be boundless spaces that include non-Lambertian, partially transmissive, textureless and finely-structured matter. A new data structure called a plenoptic octree is introduced to enable efficient (database-like) light and matter field reconstruction in devices such as mobile phones, augmented reality (AR) glasses and drones. To satisfy threshold requirements for GSR accuracy, scenes are represented as systems of partially polarized light, radiometrically interacting with matter. To demonstrate GSR, a prototype imaging polarimeter is used to reconstruct (in generalized light fields) highly reflective, hail-damaged automobile body panels. Follow-on GSR experiments are described.
Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints. This paper introduces a general approac
We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric feature grid representing t
3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to traditional generative learned models which encode the full generative pr
Image based modeling and laser scanning are two commonly used approaches in large-scale architectural scene reconstruction nowadays. In order to generate a complete scene reconstruction, an effective way is to completely cover the scene using ground
Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~etc. However, existing datasets are biased in terms of object and relationship