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.

翻译：我们引入了光纤图案, 这是使用手持 RGBD 相机以互动速率对薄质结构进行高品质扫描的第一个方法 。 短丝类结构在数学上只有嵌入 RQ3 的1D 曲线, 集成型重建在使用天体( 未知的) 曲线骨架结合深度序列( 从薄结构部分) 时最有效。 因此, 使用互补但又吵闹的颜色和深度通道, 曲线图案首先自动识别潜在薄质结构的点样板, 并将其分组成捆包, 每一个都是一组固定数的相匹配连续框架 。 然后, 算法从所有包件( 从薄结构部分) 中提取每根线性骨架曲线曲线曲线曲线曲线曲线曲线曲线曲线, 然后将所有包件的L1 组合和迭接合并成最后完整的曲线骨架。 因此, 与以前的方法不同,, 重建是通过集数据依赖的原始熔融源, 即提取的曲线骨架进行整合。 我们广泛评估了一系列具有挑战性的例子、 不同的扫描和校准设置, 以及当前的高忠诚性薄结构重建 。