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

机译:基于平面的几何和纹理优化,用于室内场景的RGB-D重建

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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.
机译:我们提出了一种新颖的方法来重建具有平面图元的RGB-D室内场景。我们的方法采用RGB-D序列和通过序列上的某些3D重建方法重建的密集粗网格作为输入,并生成轻质,低多边形的网格,具有清晰的面部纹理和清晰的特征,而不会丢失原始场景的几何细节。为此,我们首先将输入网格划分为平面图元,然后将其简化为轻量级网格,然后优化平面参数,相机姿势和纹理颜色以最大化跨帧的光度一致性,最后优化网格几何以最大化几何之间的一致性和飞机。与仅覆盖场景中较大平面区域的现有平面重建方法相比,我们的方法通过自适应平面构建整个场景而不会丢失几何细节,并在最终网格中保留了鲜明的特征。通过将其应用于几种RGB-D扫描并将其与其他最新的重建方法进行比较,我们证明了该方法的有效性。

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