10 research outputs found
Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Under mild conditions on the noise level of the measurements, rotation
averaging satisfies strong duality, which enables global solutions to be
obtained via semidefinite programming (SDP) relaxation. However, generic
solvers for SDP are rather slow in practice, even on rotation averaging
instances of moderate size, thus developing specialised algorithms is vital. In
this paper, we present a fast algorithm that achieves global optimality called
rotation coordinate descent (RCD). Unlike block coordinate descent (BCD) which
solves SDP by updating the semidefinite matrix in a row-by-row fashion, RCD
directly maintains and updates all valid rotations throughout the iterations.
This obviates the need to store a large dense semidefinite matrix. We
mathematically prove the convergence of our algorithm and empirically show its
superior efficiency over state-of-the-art global methods on a variety of
problem configurations. Maintaining valid rotations also facilitates
incorporating local optimisation routines for further speed-ups. Moreover, our
algorithm is simple to implement; see supplementary material for a
demonstration program.Comment: Accepted to CVPR 2021 as an oral presentatio
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
We present MultiBodySync, a novel, end-to-end trainable multi-body motion
segmentation and rigid registration framework for multiple input 3D point
clouds. The two non-trivial challenges posed by this multi-scan multibody
setting that we investigate are: (i) guaranteeing correspondence and
segmentation consistency across multiple input point clouds capturing different
spatial arrangements of bodies or body parts; and (ii) obtaining robust
motion-based rigid body segmentation applicable to novel object categories. We
propose an approach to address these issues that incorporates spectral
synchronization into an iterative deep declarative network, so as to
simultaneously recover consistent correspondences as well as motion
segmentation. At the same time, by explicitly disentangling the correspondence
and motion segmentation estimation modules, we achieve strong generalizability
across different object categories. Our extensive evaluations demonstrate that
our method is effective on various datasets ranging from rigid parts in
articulated objects to individually moving objects in a 3D scene, be it
single-view or full point clouds.Comment: Contact: huang-jh18mailstsinghuaeduc
Learning multiview 3D point cloud registration
We present a novel, end-to-end learnable, multiview 3D point cloud
registration algorithm. Registration of multiple scans typically follows a
two-stage pipeline: the initial pairwise alignment and the globally consistent
refinement. The former is often ambiguous due to the low overlap of neighboring
point clouds, symmetries and repetitive scene parts. Therefore, the latter
global refinement aims at establishing the cyclic consistency across multiple
scans and helps in resolving the ambiguous cases. In this paper we propose, to
the best of our knowledge, the first end-to-end algorithm for joint learning of
both parts of this two-stage problem. Experimental evaluation on well accepted
benchmark datasets shows that our approach outperforms the state-of-the-art by
a significant margin, while being end-to-end trainable and computationally less
costly. Moreover, we present detailed analysis and an ablation study that
validate the novel components of our approach. The source code and pretrained
models are publicly available under
https://github.com/zgojcic/3D_multiview_reg.Comment: CVPR2020 - Camera Read