Point Cloud to Wireframe — Knowledge Base

2019–2025

A. Point Cloud → Wireframe / Structured Geometry (Core Pipeline)

Directly relevant to the "point cloud to wire frame" problem. These papers tackle vertex detection, edge/topology reconstruction, B-Rep generation from point clouds, and end-to-end wireframe parsing — the core pipeline from raw 3D data to structured geometric output.

PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds

arXiv:2103.02766 (2021)

3D wireframe reconstruction from raw point clouds — vertex detection, localization, and edge detection pipeline.

3D Wireframe Reconstruction Vertex Detection Edge Detection Corner Localization Point Cloud to CAD

ComplexGen: CAD B-Rep Chain Complex Generation

arXiv:2205.14573 (2022)

CAD B-Rep chain complex generation from point clouds — joint vertex/edge/face + topology reconstruction.

B-Rep Chain Complex Joint Topology Reconstruction Vertex/Edge/Face Detection CAD Reverse Engineering Point Cloud to CAD

CLR-Wire: Continuous Latent Representation for 3D Curve Wireframes

arXiv:2504.19174 (2025)

3D curve wireframe generation — continuous latent representation for geometry+topology, conditions on point clouds.

3D Curve Wireframe Continuous Latent Representation Geometry-Topology Unification Neural Parametric Curve Conditional Wireframe Generation

L-CNN: End-to-End Wireframe Parsing

arXiv:1905.03246 (2019)

End-to-end 2D wireframe parsing (from images) — junction + line detection with LoI pooling.

Wireframe Parsing Junction Detection Line-of-Interest Pooling Structural AP End-to-End Vectorized Output

BrepGen: B-Rep Generative Diffusion Model

arXiv:2401.15563 (2024)

B-Rep generative diffusion model — directly generates CAD B-Rep with structured latent geometry.

B-Rep Diffusion Model Structured Latent Geometry CAD Generation Face/Edge/Loop Topology Boundary Representation

2017–2022

B. Point Cloud Feature Learning & Encoding

Learning compact vector representations and features from raw point clouds. This line of work spans autoencoders (FoldingNet, TearingNet), Transformers (Point Transformer, PCT, Point-BERT), and self-supervised masked autoencoders (Point-MAE, Point-M2AE).

Achlioptas et al.: Learning Representations and Generative Models for 3D Point Clouds

arXiv:1707.02392 (2017)

Point cloud autoencoders + generative models (GANs in latent space, GMMs) — pioneering raw point cloud learning.

Point Cloud Autoencoder Latent Space GAN Chamfer Distance 3D Generative Model Shape Interpolation

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

arXiv:1712.07262 (2017)

Point cloud AE via 2D grid folding decoder; unsupervised feature learning through reconstruction.

2D Grid Folding Unsupervised Feature Learning Point Cloud Reconstruction Chamfer Distance Grid Deformation

TearingNet: Point Cloud Autoencoder with Topology Adaptation

arXiv:2006.10187 (2020)

Topology-friendly point cloud AE — tears 2D grid to match holes and multi-object topology in reconstruction.

Topology-Aware Autoencoder 2D Grid Tearing Point Cloud Reconstruction Manifold Structure Multi-Object Topology

Point Transformer: Self-Attention Networks for 3D Point Cloud Processing

arXiv:2012.09164 (2020)

Self-attention layers for 3D point cloud segmentation & classification with vector self-attention.

Self-Attention for Point Cloud Vector Self-Attention 3D Segmentation Position Encoding Point Cloud Classification

PCT: Point Cloud Transformer

arXiv:2012.09688 (2020)

Point Cloud Transformer with offset-attention and neighbor embedding for permutation-invariant processing.

Offset-Attention Neighbor Embedding Point Cloud Transformer Global Feature Aggregation Permutation Invariance

Point-BERT: Pre-training Point Cloud Transformers

arXiv:2111.14819 (2021)

BERT-style pre-training for point cloud Transformers via masked point modeling with dVAE tokenizer.

Masked Point Modeling BERT-Style Pre-training 3D Self-Supervised Learning dVAE Tokenizer Point Cloud Transformer

Point-MAE: Masked Autoencoders for Point Cloud Pre-training

arXiv:2203.06604 (2022)

Masked autoencoders for point cloud self-supervised learning with asymmetric encoder-decoder design.

Masked Autoencoder Self-Supervised Learning Point Cloud Pre-training Asymmetric Encoder-Decoder Masked Point Reconstruction

Point-M2AE: Multi-scale Masked Autoencoders

arXiv:2205.14401 (2022)

Multi-scale masked autoencoders for hierarchical point cloud pre-training with pyramid feature learning.

Multi-Scale Masked Autoencoder Hierarchical Point Cloud Self-Supervised Pre-training Pyramid Feature Learning

2018–2023

C. Implicit 3D Surface Reconstruction

Reconstructing continuous 3D surfaces from point clouds or 2D observations via occupancy fields, signed distance functions, and neural fields. From global implicit functions (IM-NET, Occupancy Networks, DeepSDF) to local/convolutional variants (LDIF, ConvONets, LIG) and point-convolution approaches (POCO, 3DShape2VecSet).

IM-NET: Learning Implicit Fields for Generative Shape Modeling

arXiv:1812.02822 (2018)

Learning implicit fields (inside/outside) for generative shape modeling, interpolation, and completion from latent codes.

Implicit Field Occupancy Classification Shape Interpolation Generative Shape Modeling

Occupancy Networks: Learning 3D Reconstruction in Function Space

arXiv:1812.03828 (2018)

Continuous 3D reconstruction in function space (occupancy classifier) — arbitrary-resolution mesh extraction.

Occupancy Function Continuous 3D Representation Neural Implicit Surface Mesh Extraction Function Space

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

arXiv:1901.05103 (2019)

Learning continuous signed distance functions for shape representation with auto-decoder architecture.

Signed Distance Function Continuous Shape Representation Auto-decoder Shape Completion Latent Shape Code

LDIF: Local Deep Implicit Functions for 3D Shape

arXiv:1912.06126 (2019)

Structured decomposition + local implicit decoders — high-quality 3D shape representation and assembly.

Local Deep Implicit Function Structured Decomposition Shape Assembly Implicit Surface Decoder

ConvONets: Convolutional Occupancy Networks

arXiv:2003.04618 (2020)

Translation-equivariant implicit 3D reconstruction — convolutional feature grids + occupancy decoding.

Translation Equivariance Convolutional Occupancy Network 3D Surface Reconstruction Volumetric Convolution Point Cloud Conditioning

LIG: Local Implicit Grid Representations for 3D Scenes

arXiv:2003.08981 (2020)

Part-level latent embedding in a regular grid for representing complex 3D scenes with local implicit decoders.

Local Implicit Grid Part-Level Embedding 3D Scene Representation Implicit Neural Representation Grid Feature Encoding

POCO: Point Convolution for Surface Reconstruction

arXiv:2201.01831 (2022)

Point convolution for surface reconstruction — per-point latent vectors + learned interpolation for occupancy.

Point Convolution Surface Reconstruction Per-Point Latent Vector Learned Interpolation Occupancy Prediction

3DShape2VecSet: Neural Field Representation for 3D Generation

arXiv:2301.11445 (2023)

Set-of-vectors neural field representation for diffusion-based 3D shape generation with cross-attention decoding.

Set-of-Vectors Representation Neural Field Diffusion-Based Generation Cross-Attention Decoding

2020–2021

D. CAD / B-Rep Learning & Processing

Directly operating on or generating CAD boundary representations. These methods tackle the challenge of learning from the native data structures of engineering CAD — UV-grid parameterized faces/edges (UV-Net) and topological message passing on face/edge/coedge graphs (BRepNet).

UV-Net: Learning from Boundary Representations

arXiv:2006.10211 (2020)

Learning from B-Rep via UV-grid parameterization + graph neural networks for CAD feature learning.

UV-Grid Parameterization B-Rep Learning CAD Feature Learning 1D/2D Convolution on B-Rep Graph Neural Network

BRepNet: Topological Message Passing on B-Rep for CAD Segmentation

arXiv:2104.00706 (2021)

Topological message passing on B-Rep faces/edges/coedges for CAD model segmentation.

Topological Message Passing B-Rep Segmentation Face/Edge/Coedge Graph CAD Model Analysis Geometric Deep Learning

2021–2024

E. 3D Shape Generation (All Representations)

Generative models for 3D shapes using various representations — set-structured VAEs (SetVAE), textured mesh GANs (GET3D), latent point diffusion (LION), unified structured latents (SLAT), and VAE benchmarking with sharp-edge sampling (Dora-VAE).

SetVAE: Hierarchical VAE for Set-Structured Data

arXiv:2103.15619 (2021)

Hierarchical VAE for set-structured data (point cloud generation) — multi-scale latent representations.

Hierarchical VAE Set-Structured Data Point Cloud Generation Latent Hierarchical Learning Permutation-Invariant Encoding

GET3D: A Generative Model of High Quality 3D Textured Shapes

arXiv:2209.11163 (2022)

Generative model of textured 3D meshes from 2D images — DMTet + differentiable rendering.

Textured 3D Mesh Generation DMTet Differentiable Rendering 3D GAN Shape and Texture Synthesis

LION: Latent Point Diffusion Models for 3D Shape Generation

arXiv:2210.06978 (2022)

Latent point diffusion models for 3D shape generation — hierarchical VAE latent space + denoising diffusion.

Latent Point Diffusion 3D Shape Generation Hierarchical VAE Denoising Diffusion Shape Latent Space

SLAT: Structured 3D Latents for Scalable 3D Generation

arXiv:2412.01506 (2024)

Structured 3D Latents — unified latent for scalable 3D generation (Radiance Fields / 3DGS / Mesh).

Structured 3D Latents Unified 3D Generation Radiance Field 3D Gaussian Splatting Mesh Generation

Dora-VAE: Sampling & Benchmarking for 3D Shape VAEs

arXiv:2412.17808 (2024)

Sampling & benchmarking for 3D shape VAEs — sharp edge importance sampling methodology.

3D Shape VAE Sharp Edge Sampling VAE Benchmarking 3D Shape Generation Surface Reconstruction Quality

2016–2020

F. Foundational / Adjacent Methods

Foundational techniques that underpin the broader research landscape: unsupervised graph learning (VGAE), interaction graph inference from dynamics (NRI), Transformer-based set prediction (DETR), and hierarchical vector graphics generation (DeepSVG).

VGAE: Variational Graph Auto-Encoders

arXiv:1611.07308 (2016)

Variational Graph Auto-Encoders — foundational unsupervised learning on graphs for link prediction.

Graph Autoencoder Latent Variable Model Graph Convolution Link Prediction

NRI: Neural Relational Inference for Interacting Systems

arXiv:1802.04687 (2018)

Neural Relational Inference — inferring interaction graphs + dynamics from trajectories with VAE.

Neural Relational Inference Interaction Graph Trajectory Prediction Variational Autoencoder Graph Neural Network

DETR: End-to-End Object Detection with Transformers

arXiv:2005.12872 (2020)

End-to-End Object Detection with Transformers — set prediction, bipartite matching, no NMS or anchors.

Set Prediction Bipartite Matching Object Query End-to-End Detection Transformer

DeepSVG: Hierarchical Generative Network for Vector Graphics

arXiv:2007.11301 (2020)

Hierarchical generative network for vector graphics (2D SVG) animation and controllable generation.

SVG Generation Hierarchical Vector Graphics Path Decoder 2D Vector Representation