Search Results for author:
Found 13 papers, 10 papers with code
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers.
OperatorNet: Recovering 3D Shapes From Difference Operators
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices.
Effective Rotation-invariant Point CNN with Spherical Harmonics kernels
We present a novel rotation invariant architecture operating directly on point cloud data.
Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation
We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds, that is explicitly designed to preserve intrinsic shape properties.
Correspondence Learning via Linearly-invariant Embedding
However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous works in this domain, we demonstrate that learning the basis from data can both improve robustness and lead to better accuracy in challenging settings.
Ranked #7 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
Learning Delaunay Surface Elements for Mesh Reconstruction
We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.
Differentiable Surface Triangulation
Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.
Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion
Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arbitrary topologies.
SPARF: Neural Radiance Fields from Sparse and Noisy Poses
Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views.
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field.
SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection
However, given that objects occupy only a small part of a scene, finding dense candidates and generating dense representations is noisy and inefficient.
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections
In this work, we propose UniSDF, a general purpose 3D reconstruction method that can reconstruct large complex scenes with reflections.
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS
First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization.
