6 research outputs found
Comprehensive Use of Curvature for Robust and Accurate Online Surface Reconstruction
Interactive real-time scene acquisition from hand-held depth cameras has recently developed much momentum, enabling
applications in ad-hoc object acquisition, augmented reality and other fields. A key challenge to online reconstruction remains
error accumulation in the reconstructed camera trajectory, due to drift-inducing instabilities in the range scan alignments of the
underlying iterative-closest-point (ICP) algorithm. Various strategies have been proposed to mitigate that drift, including SIFT-based
pre-alignment, color-based weighting of ICP pairs, stronger weighting of edge features, and so on. In our work, we focus on surface
curvature as a feature that is detectable on range scans alone and hence does not depend on accurate multi-sensor alignment. In
contrast to previous work that took curvature into consideration, however, we treat curvature as an independent quantity that we
consistently incorporate into every stage of the real-time reconstruction pipeline, including densely curvature-weighted ICP, range
image fusion, local surface reconstruction, and rendering. Using multiple benchmark sequences, and in direct comparison to other
state-of-the-art online acquisition systems, we show that our approach significantly reduces drift, both when analyzing individual
pipeline stages in isolation, as well as seen across the online reconstruction pipeline as a whole
SurfelMeshing: Online Surfel-Based Mesh Reconstruction
We address the problem of mesh reconstruction from live RGB-D video, assuming
a calibrated camera and poses provided externally (e.g., by a SLAM system). In
contrast to most existing approaches, we do not fuse depth measurements in a
volume but in a dense surfel cloud. We asynchronously (re)triangulate the
smoothed surfels to reconstruct a surface mesh. This novel approach enables to
maintain a dense surface representation of the scene during SLAM which can
quickly adapt to loop closures. This is possible by deforming the surfel cloud
and asynchronously remeshing the surface where necessary. The surfel-based
representation also naturally supports strongly varying scan resolution. In
particular, it reconstructs colors at the input camera's resolution. Moreover,
in contrast to many volumetric approaches, ours can reconstruct thin objects
since objects do not need to enclose a volume. We demonstrate our approach in a
number of experiments, showing that it produces reconstructions that are
competitive with the state-of-the-art, and we discuss its advantages and
limitations. The algorithm (excluding loop closure functionality) is available
as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine
Intelligenc
DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data
Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. One recent approach proposes self-supervision based on non-rigid reconstruction. Unfortunately, this method fails for important cases such as highly non-rigid deformations. We first address this problem of lack of data by introducing a novel semi-supervised strategy to obtain dense inter-frame correspondences from a sparse set of annotations. This way, we obtain a large dataset of 400 scenes, over 390,000 RGB-D frames, and 2,537 densely aligned frame pairs; in addition, we provide a test set along with several metrics for evaluation. Based on this corpus, we introduce a data-driven non-rigid feature matching approach, which we integrate into an optimization-based reconstruction pipeline. Here, we propose a new neural network that operates on RGB-D frames, while maintaining robustness under large non-rigid deformations and producing accurate predictions. Our approach significantly outperforms both existing non-rigid reconstruction methods that do not use learned data terms, as well as learning-based approaches that only use self-supervision