Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes

We present a novel approach to reconstruct RGB-D indoor scene with plane primitives. Our approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed by some 3D reconstruction method on the sequence, and generate a lightweight, low-polygonal mesh with clear face textures and sharp features without losing geometry details from the original scene. To achieve this, we firstly partition the input mesh with plane primitives, simplify it into a lightweight mesh next, then optimize plane parameters, camera poses and texture colors to maximize the photometric consistency across frames, and finally optimize mesh geometry to maximize consistency between geometry and planes. Compared to existing planar reconstruction methods which only cover large planar regions in the scene, our method builds the entire scene by adaptive planes without losing geometry details and preserves sharp features in the final mesh. We demonstrate the effectiveness of our approach by applying it onto several RGB-D scans and comparing it to other state-of-the-art reconstruction methods.Comment: in International Conference on 3D Vision 2018; Models and Code: see https://github.com/chaowang15/plane-opt-rgbd. arXiv admin note: text overlap with arXiv:1905.0885

GASP : Geometric Association with Surface Patches

A fundamental challenge to sensory processing tasks in perception and robotics is the problem of obtaining data associations across views. We present a robust solution for ascertaining potentially dense surface patch (superpixel) associations, requiring just range information. Our approach involves decomposition of a view into regularized surface patches. We represent them as sequences expressing geometry invariantly over their superpixel neighborhoods, as uniquely consistent partial orderings. We match these representations through an optimal sequence comparison metric based on the Damerau-Levenshtein distance - enabling robust association with quadratic complexity (in contrast to hitherto employed joint matching formulations which are NP-complete). The approach is able to perform under wide baselines, heavy rotations, partial overlaps, significant occlusions and sensor noise. The technique does not require any priors -- motion or otherwise, and does not make restrictive assumptions on scene structure and sensor movement. It does not require appearance -- is hence more widely applicable than appearance reliant methods, and invulnerable to related ambiguities such as textureless or aliased content. We present promising qualitative and quantitative results under diverse settings, along with comparatives with popular approaches based on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201

Point Cloud Data Fusion for Real -Time Applications

This thesis presents how to register point clouds from RGB-D sensors to perform real-time 3D reconstruction of an indoor scene with the aim to build a representation of the environment useful for robot planning. Several common techniques have been explored and compared to determine benefits and drawbacks of each method for our setup. A more specific method was then developed based on this knowledge and has been compared with other methods in terms of rotation and translation errors. Finally these results are used to evaluate the method accuracy and determine in which conditions these results can be replicate

Efficient Interactive Sound Propagation in Dynamic Environments

The physical phenomenon of sound is ubiquitous in our everyday life and is an important component of immersion in interactive virtual reality applications. Sound propagation involves modeling how sound is emitted from a source, interacts with the environment, and is received by a listener. Previous techniques for computing interactive sound propagation in dynamic scenes are based on geometric algorithms such as ray tracing. However, the performance and quality of these algorithms is strongly dependent on the number of rays traced. In addition, it is difficult to acquire acoustic material properties. It is also challenging to efficiently compute spatial sound effects from the output of ray tracing-based sound propagation. These problems lead to increased latency and less plausible sound in dynamic interactive environments. In this dissertation, we propose three approaches with the goal of addressing these challenges. First, we present an approach that utilizes temporal coherence in the sound field to reuse computation from previous simulation time steps. Secondly, we present a framework for the automatic acquisition of acoustic material properties using visual and audio measurements of real-world environments. Finally, we propose efficient techniques for computing directional spatial sound for sound propagation with low latency using head-related transfer functions (HRTF). We have evaluated both the performance and subjective impact of these techniques on a variety of complex dynamic indoor and outdoor environments and observe an order-of-magnitude speedup over previous approaches. The accuracy of our approaches has been validated against real-world measurements and previous methods. The proposed techniques enable interactive simulation of sound propagation in complex multi-source dynamic environments.Doctor of Philosoph