DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time

By Richard A. Newcombe, Dieter Fox, S. M. Seitz. 2015

1,094 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

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Citations in the corpus

Self-Supervised Correspondence in Visuomotor Policy Learning (2019, positive): “Other prior work [6] has used dynamic non-rigid reconstruction [35] to address dynamic scenes.”
SE3-nets: Learning rigid body motion using deep neural networks (2016, methodology): “Another exciting direction is to use the “No Penalty” version of SE3-NETS to model nonrigid motion, exploring the use of strong regularization and locality priors in the masks [33] to improve efficiency and”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “[182] have address the nonrigid case for small-scale reconstruction.”
Dense Human Body Correspondences Using Convolutional Networks (2015, background): “With the popularization of commodity 3D scanners and recent advances in correspondence algorithms for deformable shapes, human bodies can now be easily digitized [24, 29, 11] and their performances captured using a single RGB-D sensor [23, 42].”
Deformable Shape Completion with Graph Convolutional Autoencoders (2017, background): “works of [37] and [21] have shown very impressive reconstructions, however they are limited to small motions between consecutive frames.”
Multi-Garment Net: Learning to Dress 3D People From Images (2019, background): “Without a pre-scanned template, fusion [30, 29, 55, 43, 58] trackers incrementally fuse geometry and appearance [66] to build the template on the fly.”
Learning to Dress 3D People in Generative Clothing (2019, methodology): “rgeting it to new body shapes. Similar to ClothCap, Alldieck et al. [35,36] represent clothing as an offset from the SMPL to reconstruct people in clothing from images. Combining dynamic fusion ideas [37] with SMPL, cloth capture and retargeting has been demonstrated from a single depth camera [38,3”
VNect (2017, methodology): “2014] and template-free reconstruction [Dou et al. 2016; Innmann et al. 2016; Newcombe et al. 2015; Orts-Escolano et al. 2016] has been demonstrated.”
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data (2020, background): “There is recent work on tracking deformable objects in videos such as [8, 10, 29, 32, 34, 35].”
HyperNeRF (2021, methodology): “Deformation-based NeRF variants follow the tradition established by the significant body of research on non-rigid reconstruction [Newcombe et al. 2015], and map observations of the subject onto a template of that subject.”
Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors (2021, methodology): “3(e)) as in DynamicFu-sion [33] to make sure that all of the surfaces can be covered by the ﬁnal node graph (Fig.”
Deferred neural rendering (2019, background): “A major benefit of this line of work is that the reconstructed 3D content can be visualized by standard rendering techniques, and the approach directly generalizes to 4D capture as long as non-rigid surfaces can be reliably tracked [Dou et al. 2016; Innmann et al. 2016; Newcombe et al. 2015].”
Fusion4D (2016, methodology): “Recent work has demonstrated compelling real-time reconstructions of general nonrigid scenes using a single depth camera [Zollhöfer et al. 2014; Newcombe et al. 2015].”
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM (2018, background): “One deforms the whole world in a non-rigid manner in order to include a deformable/moving object [7].”
Geometric deep learning (2016, methodology): “With the popu-larization of commodity 3D scanners and recent advances in correspondence algorithms for deformable shapes, human bodies can now be easily digitized [53, 63, 26] and their performances captured using a single RGB-D sensor [52, 91].”
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video (2020, methodology): “Such sensors can been used for 3D reconstruction and completion of rigid environments [51] and non-rigid objects [98, 50, 28, 71].”