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Surface reconstruction from point clouds: A survey and a benchmark
Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete
point cloud observation is a long-standing problem in computer vision and graphics …
point cloud observation is a long-standing problem in computer vision and graphics …
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 …
What's the situation with intelligent mesh generation: A survey and perspectives
Intelligent Mesh Generation (IMG) represents a novel and promising field of research,
utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG …
utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG …
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 …
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 …
Neural dual contouring
We introduce neural dual contouring (NDC), a new data-driven approach to mesh
reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one …
reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one …
Neural-pull: Learning signed distance functions from point clouds by learning to pull space onto surfaces
Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D
geometry processing. Several recent state-of-the-art methods address this problem using …
geometry processing. Several recent state-of-the-art methods address this problem using …
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 …
Neural shape deformation priors
Abstract We present Neural Shape Deformation Priors, a novel method for shape
manipulation that predicts mesh deformations of non-rigid objects from user-provided …
manipulation that predicts mesh deformations of non-rigid objects from user-provided …