Search Results for author: Steven Lovegrove

Found 8 papers, 4 papers with code

ERF: Explicit Radiance Field Reconstruction From Scratch

no code implementations28 Feb 2022 Samir Aroudj, Steven Lovegrove, Eddy Ilg, Tanner Schmidt, Michael Goesele, Richard Newcombe

Robustly reconstructing such a volumetric scene model with millions of unknown variables from registered scene images only is a highly non-convex and complex optimization problem.

3D Reconstruction

Identity-Disentangled Neural Deformation Model for Dynamic Meshes

no code implementations30 Sep 2021 Binbin Xu, Lingni Ma, Yuting Ye, Tanner Schmidt, Christopher D. Twigg, Steven Lovegrove

When applied to dynamically deforming shapes such as the human hands, however, they would need to preserve temporal coherence of the deformation as well as the intrinsic identity of the subject.

Disentanglement

Neural 3D Video Synthesis from Multi-view Video

1 code implementation CVPR 2022 Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, Zhaoyang Lv

We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation.

Motion Interpolation

STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering

no code implementations CVPR 2021 Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, Steven Lovegrove

We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame.

Neural Rendering

FroDO: From Detections to 3D Objects

no code implementations11 May 2020 Kejie Li, Martin Rünz, Meng Tang, Lingni Ma, Chen Kong, Tanner Schmidt, Ian Reid, Lourdes Agapito, Julian Straub, Steven Lovegrove, Richard Newcombe

We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner.

3D Reconstruction Object Reconstruction +1

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

5 code implementations CVPR 2019 Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven Lovegrove

In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.

3D Reconstruction 3D Shape Representation

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