3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for
generative diffusion models. Our shape representation can encode 3D shapes given as …
generative diffusion models. Our shape representation can encode 3D shapes given as …
Neurbf: A neural fields representation with adaptive radial basis functions
We present a novel type of neural fields that uses general radial bases for signal
representation. State-of-the-art neural fields typically rely on grid-based representations for …
representation. State-of-the-art neural fields typically rely on grid-based representations for …
Learning consistency-aware unsigned distance functions progressively from raw point clouds
Surface reconstruction for point clouds is an important task in 3D computer vision. Most of
the latest methods resolve this problem by learning signed distance functions (SDF) from …
the latest methods resolve this problem by learning signed distance functions (SDF) from …
Towards better gradient consistency for neural signed distance functions via level set alignment
Neural signed distance functions (SDFs) have shown remarkable capability in representing
geometry with details. However, without signed distance supervision, it is still a challenge to …
geometry with details. However, without signed distance supervision, it is still a challenge to …
3dilg: Irregular latent grids for 3d generative modeling
We propose a new representation for encoding 3D shapes as neural fields. The
representation is designed to be compatible with the transformer architecture and to benefit …
representation is designed to be compatible with the transformer architecture and to benefit …
Reconstructing surfaces for sparse point clouds with on-surface priors
It is an important task to reconstruct surfaces from 3D point clouds. Current methods are able
to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single point …
to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single point …
Neaf: Learning neural angle fields for point normal estimation
Normal estimation for unstructured point clouds is an important task in 3D computer vision.
Current methods achieve encouraging results by mapping local patches to normal vectors or …
Current methods achieve encouraging results by mapping local patches to normal vectors or …
Unsupervised inference of signed distance functions from single sparse point clouds without learning priors
It is vital to infer signed distance functions (SDFs) from 3D point clouds. The latest methods
rely on generalizing the priors learned from large scale supervision. However, the learned …
rely on generalizing the priors learned from large scale supervision. However, the learned …
Gridpull: Towards scalability in learning implicit representations from 3d point clouds
Learning implicit representations has been a widely used solution for surface reconstruction
from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a …
from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a …
HSurf-Net: Normal estimation for 3D point clouds by learning hyper surfaces
We propose a novel normal estimation method called HSurf-Net, which can accurately
predict normals from point clouds with noise and density variations. Previous methods focus …
predict normals from point clouds with noise and density variations. Previous methods focus …