论文标题
RidgesFM：在深度不确定性下通过鲁棒成对匹配的运动结构
RidgeSfM: Structure from Motion via Robust Pairwise Matching Under Depth Uncertainty
论文作者
论文摘要
我们考虑了同时估算室内场景的大量图像的密集深度图和相机姿势的问题。虽然经典的SFM管道依赖于两步方法，在该方法中，首先使用束调整估算摄像机以使随之而来的多视图立体阶段进行基础，但我们的姿势和密集的重建都是变化的束调节器的直接输出。为此，我们使用有限数量的基础“深度平面”以单眼方式通过深网进行了线性组合来参数。使用一组高质量的稀疏关键匹配，我们优化了深度平面和相机姿势的人均线性组合，以形成几何一致的关键云。尽管我们的束调整仅考虑稀疏关键点，但基本平面的推断线性系数立即为我们提供了密集的深度图。 RidgesFM能够集体对齐数百个帧，这是比最近的记忆深度替代方案的主要优势，该替代方案最多可以在10帧中对齐。定量比较表明，性能优于最先进的大规模SFM管道。
We consider the problem of simultaneously estimating a dense depth map and camera pose for a large set of images of an indoor scene. While classical SfM pipelines rely on a two-step approach where cameras are first estimated using a bundle adjustment in order to ground the ensuing multi-view stereo stage, both our poses and dense reconstructions are a direct output of an altered bundle adjuster. To this end, we parametrize each depth map with a linear combination of a limited number of basis "depth-planes" predicted in a monocular fashion by a deep net. Using a set of high-quality sparse keypoint matches, we optimize over the per-frame linear combinations of depth planes and camera poses to form a geometrically consistent cloud of keypoints. Although our bundle adjustment only considers sparse keypoints, the inferred linear coefficients of the basis planes immediately give us dense depth maps. RidgeSfM is able to collectively align hundreds of frames, which is its main advantage over recent memory-heavy deep alternatives that can align at most 10 frames. Quantitative comparisons reveal performance superior to a state-of-the-art large-scale SfM pipeline.
