LiDAR Point Clouds to 3-D Urban Models A Review
R Wang, J Peethambaran… - IEEE Journal of Selected …, 2018 - ieeexplore.ieee.org
Three-dimensional (3-D) urban models are an integral part of numerous applications, such
as urban planning and performance simulation, mapping and visualization, emergency …
as urban planning and performance simulation, mapping and visualization, emergency …
State of the art in surface reconstruction from point clouds
M Berger, A Tagliasacchi, LM Seversky… - … Conference of the …, 2014 - infoscience.epfl.ch
The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …
The traditional problem addressed by surface reconstruction is to recover the digital …
Deep local shapes: Learning local sdf priors for detailed 3d reconstruction
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in
machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a …
machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a …
Local deep implicit functions for 3d shape
The goal of this project is to learn a 3D shape representation that enables accurate surface
reconstruction, compact storage, efficient computation, consistency for similar shapes …
reconstruction, compact storage, efficient computation, consistency for similar shapes …
Abc: A big cad model dataset for geometric deep learning
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD)
models for research of geometric deep learning methods and applications. Each model is a …
models for research of geometric deep learning methods and applications. Each model is a …
Learning shape templates with structured implicit functions
Template 3D shapes are useful for many tasks in graphics and vision, including fitting
observation data, analyzing shape collections, and transferring shape attributes. Because of …
observation data, analyzing shape collections, and transferring shape attributes. Because of …
A survey of surface reconstruction from point clouds
M Berger, A Tagliasacchi, LM Seversky… - Computer graphics …, 2017 - Wiley Online Library
The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …
The traditional problem addressed by surface reconstruction is to recover the digital …
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 …
Deep implicit moving least-squares functions for 3D reconstruction
Point set is a flexible and lightweight representation widely used for 3D deep learning.
However, their discrete nature prevents them from representing continuous and fine …
However, their discrete nature prevents them from representing continuous and fine …
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 …