3D Shape Representation
38 papers with code • 0 benchmarks • 4 datasets
Image: MeshNet
Benchmarks
These leaderboards are used to track progress in 3D Shape Representation
Objectron
Dynamic FAUST
KeypointNet
Foot3D
Latest papers with no code
Multiplicative Fourier Level of Detail
We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network.
LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and Scenes
To capture geometry details, current mainstream methods divide 3D shapes into local regions and then learn each one with a local latent code via a decoder, where the decoder shares the geometric similarities among different local regions.
DMNet: Delaunay Meshing Network for 3D Shape Representation
We model the Delaunay triangulation as a dual graph, extract local geometric information from the points, and embed it into the structural representation of Delaunay triangulation in an organic way, benefiting fine-grained details reconstruction.
Multi-View Photometric Stereo Revisited
The proposed approach in this paper exploits the benefit of uncertainty modeling in a deep neural network for a reliable fusion of photometric stereo (PS) and multi-view stereo (MVS) network predictions.
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction
3D reconstruction from images has wide applications in Virtual Reality and Automatic Driving, where the precision requirement is very high.
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation
We propose Omnidirectional Distance Fields (ODFs), a new 3D shape representation that encodes geometry by storing the depth to the object's surface from any 3D position in any viewing direction.
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image.
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
In this work, we aim to accelerate inference and training of coordinate-based MLPs for implicit neural representations by proposing a new split MLP architecture, CoordX.
Taylor3DNet: Fast 3D Shape Inference With Landmark Points Based Taylor Series
Taylor3DNet exploits a set of discrete landmark points and their corresponding Taylor series coefficients to represent the implicit field of a 3D shape, and the number of landmark points is independent of the resolution of the iso-surface extraction.
Cloud Sphere: A 3D Shape Representation via Progressive Deformation
In the area of 3D shape analysis, the geometric properties of a shape have long been studied.
