Temporally Coherent General Dynamic Scene Reconstruction

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 approach to obtain a 4D representation of complex dynamic scenes from multi-view wide-baseline static or moving cameras without prior knowledge of the scene structure, appearance, or illumination. Contributions of the work are: An automatic method for initial coarse reconstruction to initialize joint estimation; Sparse-to-dense temporal correspondence integrated with joint multi-view segmentation and reconstruction to introduce temporal coherence; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes by introducing shape constraint. Comparison with state-of-the-art approaches on a variety of complex indoor and outdoor scenes, demonstrates improved accuracy in both multi-view segmentation and dense reconstruction. This paper demonstrates unsupervised reconstruction of complete temporally coherent 4D scene models with improved non-rigid object segmentation and shape reconstruction and its application to free-viewpoint rendering and virtual reality.Comment: Submitted to IJCV 2019. arXiv admin note: substantial text overlap with arXiv:1603.0338

Structured Indoor Modeling

In this dissertation, we propose data-driven approaches to reconstruct 3D models for indoor scenes which are represented in a structured way (e.g., a wall is represented by a planar surface and two rooms are connected via the wall). The structured representation of models is more application ready than dense representations (e.g., a point cloud), but poses additional challenges for reconstruction since extracting structures requires high-level understanding about geometries. To address this challenging problem, we explore two common structural regularities of indoor scenes: 1) most indoor structures consist of planar surfaces (planarity), and 2) structural surfaces (e.g., walls and floor) can be represented by a 2D floorplan as a top-down view projection (orthogonality). With breakthroughs in data capturing techniques, we develop automated systems to tackle structured modeling problems, namely piece-wise planar reconstruction and floorplan reconstruction, by learning shape priors (i.e., planarity and orthogonality) from data. With structured representations and production-level quality, the reconstructed models have an immediate impact on many industrial applications

Data-driven depth and 3D architectural layout estimation of an interior environment from monocular panoramic input

Recent years have seen significant interest in the automatic 3D reconstruction of indoor scenes, leading to a distinct and very-active sub-field within 3D reconstruction. The main objective is to convert rapidly measured data representing real-world indoor environments into models encompassing geometric, structural, and visual abstractions. This thesis focuses on the particular subject of extracting geometric information from single panoramic images, using either visual data alone or sparse registered depth information. The appeal of this setup lies in the efficiency and cost-effectiveness of data acquisition using 360o images. The challenge, however, is that creating a comprehensive model from mostly visual input is extremely difficult, due to noise, missing data, and clutter. My research has concentrated on leveraging prior information, in the form of architectural and data-driven priors derived from large annotated datasets, to develop end-to-end deep learning solutions for specific tasks in the structured reconstruction pipeline. My first contribution consists in a deep neural network architecture for estimating a depth map from a single monocular indoor panorama, operating directly on the equirectangular projection. Leveraging the characteristics of indoor 360-degree images and recognizing the impact of gravity on indoor scene design, the network efficiently encodes the scene into vertical spherical slices. By exploiting long- and short- term relationships among these slices, it recovers an equirectangular depth map directly from the corresponding RGB image. My second contribution generalizes the approach to handle multimodal input, also covering the situation in which the equirectangular input image is paired with a sparse depth map, as provided from common capture setups. Depth is inferred using an efficient single-branch network with a dynamic gating system, processing both dense visual data and sparse geometric data. Additionally, a new augmentation strategy enhances the model's robustness to various types of sparsity, including those from structured light sensors and LiDAR setups. While the first two contributions focus on per-pixel geometric information, my third contribution addresses the recovery of the 3D shape of permanent room surfaces from a single panoramic image. Unlike previous methods, this approach tackles the problem in 3D, expanding the reconstruction space. It employs a graph convolutional network to directly infer the room structure as a 3D mesh, deforming a graph- encoded tessellated sphere mapped to the spherical panorama. Gravity- aligned features are actively incorporated using a projection layer with multi-head self-attention, and specialized losses guide plausible solutions in the presence of clutter and occlusions. The benchmarks on publicly available data show that all three methods provided significant improvements over the state-of-the-art