Climate studies often rely on remotely sensed images to retrieve 2-D maps of cloud properties. To advance volumetric analysis, we focus on recovering the 3-D heterogeneous extinction coefficient field of shallow clouds using multiview remote sensing data. Climate research requires large-scale worldwide statistics. To enable scalable data processing, previous deep neural networks (DNNs) can infer at spaceborne remote sensing downlink rates. However, prior methods are limited to a fixed solar illumination direction. In this work, we introduce the first scalable DNN-based system for 3-D cloud retrieval that accommodates varying camera positions and solar directions. By integrating multiview cloud intensity images with camera position and solar direction data, we achieve greater flexibility in recovery. Training of the DNN is performed by a novel two-stage scheme to address the high number of degrees of freedom in this problem. Our approach shows substantial improvements over previous state-of-the-art methods, particularly in handling variations in the sun’s zenith angle.

Inverse problems in scientific imaging often seek physical characterization of heterogeneous scene materials. The scene is thus represented by physical quantities, such as the density and sizes of particles (microphysics) across a domain. Moreover, the forward image formation model is physical. An important case is that of clouds, where microphysics in three dimensions (3D) dictate the cloud dynamics, lifetime and albedo, with implications to Earth's energy balance, sustainable energy and rainfall. Current methods, however, recover very degenerate representations of microphysics. To enable 3D volumetric recovery of all the required microphysical parameters, we introduce the neural microphysics field (NeMF). It is based on a deep neural network, whose input is multi-view polarization images. NeMF is pre-trained through supervised learning. Training relies on polarized radiative transfer, and noise modeling in polarization-sensitive sensors. The results offer unprecedented recovery, including droplet effective variance. We test NeMF in rigorous simulations and demonstrate it using real-world polarization-image data.

Scattering-based computed tomography (CT) recovers a heterogeneous volumetric scattering medium using images taken from multiple directions. It is a nonlinear problem. Prior art mainly approached it by explicit physics-based optimization of image-fitting, being slow and difficult to scale. Scale is particularly important when the objects constitute large cloud fields, where volumetric recovery is important for climate studies. Besides speed, imaging and recovery need to be flexible, to efficiently handle variable viewing geometries and resolutions. These can be caused by perturbation in camera poses or fusion of data from different types of observational sensors. There is a need for fast variable imaging projection scattering tomography of clouds (VIP-CT). We develop a learning-based solution, using a deep-neural network (DNN) which trains on a large physics-based labeled volumetric dataset. The DNN parameters are oblivious to the domain scale, hence the DNN can work with arbitrarily large domains. VIP-CT offers much better quality than the state of the art. The inference speed and flexibility of VIP-CT make it effectively real-time in the context of spaceborne observations. The paper is the first to demonstrate CT of a real cloud using empirical data directly in a DNN. VIP-CT may offer a model for a learning-based solution to nonlinear CT problems in other scientific domains. Our code is available online.

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our compression-transformer(C-Former ),reducing the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth’s climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. Spatiotemporal CT is achieved using gradient-based optimization, which accounts for the correlation time of the dynamic object content. We demonstrate this in physics-based simulations and on experimental real-world data.