Search Results for author:
Found 14 papers, 12 papers with code
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Specifically, we train a neural network to pull query 3D locations to their closest points on the surface using the predicted signed distance values and the gradient at the query locations, both of which are computed by the network itself.
3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds
To address this problem, we present a novel and efficient self-supervised point cloud representation learning framework, named 3D Occlusion Auto-Encoder (3D-OAE), to facilitate the detailed supervision inherited in local regions and global shapes.
Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors
Our key idea is to infer signed distances by pushing both the query projections to be on the surface and the projection distance to be the minimum.
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
To reconstruct a surface at a specific query location at inference time, these methods then match the local reconstruction target by searching for the best match in the local prior space (by optimizing the parameters encoding the local context) at the given query location.
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds
In this paper, we propose a novel method to learn consistency-aware unsigned distance functions directly from raw point clouds.
NeAF: Learning Neural Angle Fields for Point Normal Estimation
To resolve these issues, we propose an implicit function to learn an angle field around the normal of each point in the spherical coordinate system, which is dubbed as Neural Angle Fields (NeAF).
Ranked #3 on
Surface Normals Estimation
on PCPNet
Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment
Our insight is to propagate the zero level set to everywhere in the field through consistent gradients to eliminate uncertainty in the field that is caused by the discreteness of 3D point clouds or the lack of observations from multi-view images.
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
However, without ground truth signed distances, point normals or clean point clouds, current methods still struggle from learning SDFs from noisy point clouds.
Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection
We pull the non-zero level sets onto the zero level set with gradient constraints which align gradients over different level sets and correct unsigned distance errors on the zero level set, leading to a smoother and more continuous unsigned distance field.
Uni3D: Exploring Unified 3D Representation at Scale
Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.
Ranked #1 on
Zero-shot 3D classification
on Objaverse LVIS
(using extra training data)
GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation
We justify that the refined 3D geometric priors aid in the 3D-aware capability of 2D diffusion priors, which in turn provides superior guidance for the refinement of 3D geometric priors.
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
To address these problems, we propose to learn a structured cross-modality latent space to represent pixel features and 3D features via a differentiable probabilistic PnP solver.
Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling
At inference time, we randomly sample queries around the sparse point cloud, and project these query points onto the zero-level set of the learned implicit field to generate a dense point cloud.
UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusion
In this work, we present UDiFF, a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally.
