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We propose a new system for automatic 2D floorplan reconstruction that is enabled by SALVe, our novel pairwise learned alignment verifier. The inputs to our system are sparsely located 360$^\circ$ panoramas, whose semantic features (windows, doors, and openings) are inferred and used to hypothesize pairwise room adjacency or overlap. SALVe initializes a pose graph, which is subsequently optimized using GTSAM. Once the room poses are computed, room layouts are inferred using HorizonNet, and the floorplan is constructed by stitching the most confident layout boundaries. We validate our system qualitatively and quantitatively as well as through ablation studies, showing that it outperforms state-of-the-art SfM systems in completeness by over 200%, without sacrificing accuracy. Our results point to the significance of our work: poses of 81% of panoramas are localized in the first 2 connected components (CCs), and 89% in the first 3 CCs. Code and models are publicly available at https://github.com/zillow/salve., Comment: Accepted at ECCV 2022

In this paper, we address the problem of wide-baseline camera pose estimation from a group of 360$^\circ$ panoramas under upright-camera assumption. Recent work has demonstrated the merit of deep-learning for end-to-end direct relative pose regression in 360$^\circ$ panorama pairs [11]. To exploit the benefits of multi-view logic in a learning-based framework, we introduce Graph-CoVis, which non-trivially extends CoVisPose [11] from relative two-view to global multi-view spherical camera pose estimation. Graph-CoVis is a novel Graph Neural Network based architecture that jointly learns the co-visible structure and global motion in an end-to-end and fully-supervised approach. Using the ZInD [4] dataset, which features real homes presenting wide-baselines, occlusion, and limited visual overlap, we show that our model performs competitively to state-of-the-art approaches.

While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary. To tackle this problem, we propose a novel uncertainty-guided approach for layout boundary estimation introducing new two-stage CNN architecture termed U2RLE. The initial stage predicts both floor-wall boundary and its uncertainty and is followed by the refinement of boundaries with high positional uncertainty using a different, distance-aware loss. Finally, outputs from the two stages are merged to produce the room layout. Experiments using ZInD and Structure3D datasets show that U2RLE improves over current state-of-the-art, being able to handle both near and far walls better. In particular, U2RLE outperforms current state-of-the-art techniques for the most distant walls., Comment: To be Appear on CVPR 2023

In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP2) transformer and a novel Cross-Perspective Projection (CP2) layer. The stereo-view SP2 transformer is used to implicitly infer correspondences between views, and can handle noisy poses. The pose-aware CP2 layer is designed to render features from the adjacent view to the anchor (reference) view, in order to perform view fusion and estimate the visible layout. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art layout estimators, especially for large and complex room spaces., Comment: Accepted at CVPR 2022

Realizing van der Waals (vdW) epitaxy in the 80s represents a breakthrough that circumvents the stringent lattice matching and processing compatibility requirements in conventional covalent heteroepitaxy. However, due to the weak vdW interactions, there is little control over film qualities by the substrate. Typically, discrete domains with a spread of misorientation angles are formed, limiting the applicability of vdW epitaxy. Here we report the epitaxial growth of monocrystalline, covalent Cr5Te8 2D crystals on monolayer vdW WSe2 by chemical vapor deposition, driven by interfacial dative bond formation. The lattice of Cr5Te8, with a lateral dimension of a few ten microns, is fully commensurate with that of WSe2 via 3 x 3 (Cr5Te8)-7 x 7 (WSe2) supercell matching, forming a single crystalline moire superlattice. Our work has established a conceptually distinct paradigm of thin film epitaxy termed dative epitaxy, which takes full advantage of covalent epitaxy with chemical bonding for fixing the atomic registry and crystal orientation, while circumventing its stringent lattice matching and processing compatibility requirements; conversely, it ensures the full flexibility of vdW epitaxy, while avoiding its poor orientation control. Cr5Te8 2D crystals grown by dative epitaxy exhibit square magnetic hysteresis, suggesting minimized interfacial defects that can serve as pinning sites.

We demonstrate spatial mapping of the local and nano-scale structure of thin film objects using spatially resolved PDF analysis of synchrotron x-ray diffraction data. This is demonstrated in a lab-on-chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink-jet liquid handling system. We present a software implementation of the whole protocol including an approach for automated data acquisition and analyis using the atomic pair distribution function (PDF) method. The protocol software can handle semi-automated data reduction, normalization and modelling, with user-defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions it was possible to build images of the 2D object containing using different quantities for contrast, allowing us to determine the spatial map relating to different aspects of the local structure on the array., Comment: 6 pages, 6 figures

The process of CO2 valorization, all the way from capture of CO2 to its electrochemical upgrade, requires significant inputs in each of the capture, upgrade, and separation steps. The gas phase CO2 feed following the capture-and-release stage and into the CO2 electroreduction stage produce a large waste of CO2, between 80 and 95% of CO2 is wasted due to carbonate formation or electrolyte crossover, that adds cost and energy consumption to the CO2 management aspect of the system. Here we report an electrolyzer that instead directly upgrades carbonate electrolyte from CO2 capture solution to syngas, achieving 100% carbon utilization across the system. A bipolar membrane is used to produce proton in situ, under applied potential, which facilitates CO2 releasing at the membrane:catalyst interface from the carbonate solution. Using an Ag catalyst, we generate pure syngas at a 3 to 1 H2 to CO ratio, with no CO2 dilution at the gas outlet, at a current density of 150 mA cm-2, and achieve a full cell energy efficiency of 35%. The direct carbonate cell was stable under a continuous 145 h of catalytic operation at ca. 180 mA cm-2. The work demonstrates that coupling CO2 electrolysis directly with a CO2 capture system can accelerate the path towards viable CO2 conversion technologies.