Example-based Image Recoloring in Indoor Environment

Color structure of a home scene image closely relates to the material properties of its local regions. Existing color migration methods typically fail to fully infer the correlation between the coloring of local home scene regions, leading to a local blur problem. In this paper, we propose a color migration framework for home scene images. It picks the coloring from a template image and transforms such coloring to a home scene image through a simple interaction. Our framework comprises three main parts. First, we carry out an interactive segmentation to divide an image into local regions and extract their corresponding colors. Second, we generate a matching color table by sampling the template image according to the color structure of the original home scene image. Finally, we transform colors from the matching color table to the target home scene image with the boundary transition maintained. Experimental results show that our method can effectively transform the coloring of a scene matching with the color composition of a given natural or interior scenery

Deep Depth Completion of a Single RGB-D Image

The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of extensive experiments with a new depth completion benchmark dataset, where holes are filled in training data through the rendering of surface reconstructions created from multiview RGB-D scans. Experiments with different network inputs, depth representations, loss functions, optimization methods, inpainting methods, and deep depth estimation networks show that our proposed approach provides better depth completions than these alternatives.Comment: Accepted by CVPR2018 (Spotlight). Project webpage: http://deepcompletion.cs.princeton.edu/ This version includes supplementary materials which provide more implementation details, quantitative evaluation, and qualitative results. Due to file size limit, please check project website for high-res pape

C2Ideas: Supporting Creative Interior Color Design Ideation with Large Language Model

Interior color design is a creative process that endeavors to allocate colors to furniture and other elements within an interior space. While much research focuses on generating realistic interior designs, these automated approaches often misalign with user intention and disregard design rationales. Informed by a need-finding preliminary study, we develop C2Ideas, an innovative system for designers to creatively ideate color schemes enabled by an intent-aligned and domain-oriented large language model. C2Ideas integrates a three-stage process: Idea Prompting stage distills user intentions into color linguistic prompts; Word-Color Association stage transforms the prompts into semantically and stylistically coherent color schemes; and Interior Coloring stage assigns colors to interior elements complying with design principles. We also develop an interactive interface that enables flexible user refinement and interpretable reasoning. C2Ideas has undergone a series of indoor cases and user studies, demonstrating its effectiveness and high recognition of interactive functionality by designers.Comment: 26 pages, 11 figure

LEGO-Net: Learning Regular Rearrangements of Objects in Rooms

Humans universally dislike the task of cleaning up a messy room. If machines were to help us with this task, they must understand human criteria for regular arrangements, such as several types of symmetry, co-linearity or co-circularity, spacing uniformity in linear or circular patterns, and further inter-object relationships that relate to style and functionality. Previous approaches for this task relied on human input to explicitly specify goal state, or synthesized scenes from scratch -- but such methods do not address the rearrangement of existing messy scenes without providing a goal state. In this paper, we present LEGO-Net, a data-driven transformer-based iterative method for learning regular rearrangement of objects in messy rooms. LEGO-Net is partly inspired by diffusion models -- it starts with an initial messy state and iteratively "de-noises'' the position and orientation of objects to a regular state while reducing the distance traveled. Given randomly perturbed object positions and orientations in an existing dataset of professionally-arranged scenes, our method is trained to recover a regular re-arrangement. Results demonstrate that our method is able to reliably rearrange room scenes and outperform other methods. We additionally propose a metric for evaluating regularity in room arrangements using number-theoretic machinery.Comment: Project page: https://ivl.cs.brown.edu/projects/lego-ne

Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes

This report surveys advances in deep learning-based modeling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modeling tasks based on these representations. Specifically, we focus on the analysis and synthesis of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks -- 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model-driven methods that allow for human-centric, progressive scene synthesis. We identify the challenges involved in modeling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state-of-the-art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output, etc., providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse.Comment: Published in Computer Graphics Forum, Aug 202

Multi feature-rich synthetic colour to improve human visual perception of point clouds

Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431F 2022/08Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Universidade de Vigo/CISU

Indoor Mapping and Reconstruction with Mobile Augmented Reality Sensor Systems

Augmented Reality (AR) ermöglicht es, virtuelle, dreidimensionale Inhalte direkt innerhalb der realen Umgebung darzustellen. Anstatt jedoch beliebige virtuelle Objekte an einem willkürlichen Ort anzuzeigen, kann AR Technologie auch genutzt werden, um Geodaten in situ an jenem Ort darzustellen, auf den sich die Daten beziehen. Damit eröffnet AR die Möglichkeit, die reale Welt durch virtuelle, ortbezogene Informationen anzureichern. Im Rahmen der vorliegenen Arbeit wird diese Spielart von AR als "Fused Reality" definiert und eingehend diskutiert. Der praktische Mehrwert, den dieses Konzept der Fused Reality bietet, lässt sich gut am Beispiel seiner Anwendung im Zusammenhang mit digitalen Gebäudemodellen demonstrieren, wo sich gebäudespezifische Informationen - beispielsweise der Verlauf von Leitungen und Kabeln innerhalb der Wände - lagegerecht am realen Objekt darstellen lassen. Um das skizzierte Konzept einer Indoor Fused Reality Anwendung realisieren zu können, müssen einige grundlegende Bedingungen erfüllt sein. So kann ein bestimmtes Gebäude nur dann mit ortsbezogenen Informationen augmentiert werden, wenn von diesem Gebäude ein digitales Modell verfügbar ist. Zwar werden größere Bauprojekt heutzutage oft unter Zuhilfename von Building Information Modelling (BIM) geplant und durchgeführt, sodass ein digitales Modell direkt zusammen mit dem realen Gebäude ensteht, jedoch sind im Falle älterer Bestandsgebäude digitale Modelle meist nicht verfügbar. Ein digitales Modell eines bestehenden Gebäudes manuell zu erstellen, ist zwar möglich, jedoch mit großem Aufwand verbunden. Ist ein passendes Gebäudemodell vorhanden, muss ein AR Gerät außerdem in der Lage sein, die eigene Position und Orientierung im Gebäude relativ zu diesem Modell bestimmen zu können, um Augmentierungen lagegerecht anzeigen zu können. Im Rahmen dieser Arbeit werden diverse Aspekte der angesprochenen Problematik untersucht und diskutiert. Dabei werden zunächst verschiedene Möglichkeiten diskutiert, Indoor-Gebäudegeometrie mittels Sensorsystemen zu erfassen. Anschließend wird eine Untersuchung präsentiert, inwiefern moderne AR Geräte, die in der Regel ebenfalls über eine Vielzahl an Sensoren verfügen, ebenfalls geeignet sind, als Indoor-Mapping-Systeme eingesetzt zu werden. Die resultierenden Indoor Mapping Datensätze können daraufhin genutzt werden, um automatisiert Gebäudemodelle zu rekonstruieren. Zu diesem Zweck wird ein automatisiertes, voxel-basiertes Indoor-Rekonstruktionsverfahren vorgestellt. Dieses wird außerdem auf der Grundlage vierer zu diesem Zweck erfasster Datensätze mit zugehörigen Referenzdaten quantitativ evaluiert. Desweiteren werden verschiedene Möglichkeiten diskutiert, mobile AR Geräte innerhalb eines Gebäudes und des zugehörigen Gebäudemodells zu lokalisieren. In diesem Kontext wird außerdem auch die Evaluierung einer Marker-basierten Indoor-Lokalisierungsmethode präsentiert. Abschließend wird zudem ein neuer Ansatz, Indoor-Mapping Datensätze an den Achsen des Koordinatensystems auszurichten, vorgestellt