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Found 8 papers, 4 papers with code
ERF: Explicit Radiance Field Reconstruction From Scratch
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.
Identity-Disentangled Neural Deformation Model for Dynamic Meshes
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.
Neural 3D Video Synthesis from Multi-view Video
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.
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
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.
FroDO: From Detections to 3D Objects
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.
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception.
The Replica Dataset: A Digital Replica of Indoor Spaces
We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
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.
