We introduce CurveFusion, the first approach for high quality scanning of thin structures at interactive rates using a handheld RGBD camera. Thin filament-like structures are mathematically just 1D curves embedded in R^3, and integration-based reconstruction works best when depth sequences (from the thin structure parts) are fused using the object's (unknown) curve skeleton. Thus, using the complementary but noisy color and depth channels, CurveFusion first automatically identifies point samples on potential thin structures and groups them into bundles, each being a group of a fixed number of aligned consecutive frames. Then, the algorithm extracts per-bundle skeleton curves using L1 axes, and aligns and iteratively merges the L1 segments from all the bundles to form the final complete curve skeleton. Thus, unlike previous methods, reconstruction happens via integration along a data-dependent fusion primitive, i.e., the extracted curve skeleton. We extensively evaluate CurveFusion on a range of challenging examples, different scanner and calibration settings, and present high fidelity thin structure reconstructions previously just not possible from raw RGBD sequences.

中文翻译：

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CurveFusion：从 RGBD 序列重建薄结构

我们介绍了 CurveFusion，这是第一种使用手持式 RGBD 相机以交互速率对薄结构进行高质量扫描的方法。细丝状结构在数学上只是嵌入在 R^3 中的一维曲线，当深度序列（来自细结构部分）使用对象的（未知）曲线骨架融合时，基于积分的重建效果最佳。因此，使用互补但嘈杂的颜色和深度通道，CurveFusion 首先自动识别潜在薄结构上的点样本，并将它们分组为束，每个束都是一组固定数量的对齐连续帧。然后，该算法使用 L1 轴提取每束骨架曲线，并对齐和迭代合并来自所有束的 L1 段以形成最终的完整曲线骨架。因此，与以前的方法不同，重建是通过沿依赖于数据的融合原语（即提取的曲线骨架）进行整合而发生的。我们在一系列具有挑战性的示例、不同的扫描仪和校准设置上广泛评估了 CurveFusion，并呈现了以前无法从原始 RGBD 序列中进行的高保真薄结构重建。