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SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-58529-7_44openalex-snapshot

PUGeo-Net: A Geometry-Centric Network for 3D Point Cloud Upsampling

inferredcites →https://doi.org/10.1007/978-3-030-58555-6_24openalex-snapshot

Progressive Point Cloud Deconvolution Generation Network

inferredcites →https://doi.org/10.1016/j.cirp.2017.04.044openalex-snapshot

Point cloud based robot cell calibration

inferredcites →https://doi.org/10.1016/j.measurement.2018.09.022openalex-snapshot

Automatic reconstruction method for large scene based on multi-site point cloud stitching

inferredcites →https://doi.org/10.1109/3dv.2018.00088openalex-snapshot

PCN: Point Completion Network

inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

inferredcites →https://doi.org/10.1109/cvpr.2014.491openalex-snapshot

FAUST: Dataset and Evaluation for 3D Mesh Registration

inferredcites →https://doi.org/10.1109/cvpr.2018.00295openalex-snapshot

PU-Net: Point Cloud Upsampling Network

inferredcites →https://doi.org/10.1109/cvpr.2018.00375openalex-snapshot

3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare

inferredcites →https://doi.org/10.1109/cvpr.2018.00411openalex-snapshot

Neural 3D Mesh Renderer

inferredcites →https://doi.org/10.1109/cvpr.2018.00470openalex-snapshot

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition

inferredcites →https://doi.org/10.1109/cvpr.2018.00478openalex-snapshot

Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

inferredcites →https://doi.org/10.1109/cvpr.2018.00767openalex-snapshot

Nonlinear 3D Face Morphable Model

inferredcites →https://doi.org/10.1109/cvpr.2018.00874openalex-snapshot

Unsupervised Training for 3D Morphable Model Regression

inferredcites →https://doi.org/10.1109/cvpr.2019.00571openalex-snapshot

PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing

inferredcites →https://doi.org/10.1109/cvpr.2019.00611openalex-snapshot

Patch-Based Progressive 3D Point Set Upsampling

inferredcites →https://doi.org/10.1109/cvpr.2019.00655openalex-snapshot

L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving

inferredcites →https://doi.org/10.1109/cvpr.2019.00864openalex-snapshot

Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00187openalex-snapshot

Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis

inferredcites →https://doi.org/10.1109/ic3d.2016.7823453openalex-snapshot

Explorations for real-time point cloud rendering of natural scenes in virtual reality

inferredcites →https://doi.org/10.1109/iccv.1995openalex-snapshot

Proceedings of IEEE International Conference on Computer Vision

inferredcites →https://doi.org/10.1109/iccv.2019.00292openalex-snapshot

LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis

inferredcites →https://doi.org/10.1109/iccv.2019.00464openalex-snapshot

PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows

inferredcites →https://doi.org/10.1109/iccv.2019.00730openalex-snapshot

PU-GAN: A Point Cloud Upsampling Adversarial Network

inferredcites →https://doi.org/10.1109/iccv.2019.00780openalex-snapshot

Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning

inferredcites →https://doi.org/10.1109/icra.2019.8793609openalex-snapshot

3D Keypoint Repeatability for Heterogeneous Multi-Robot SLAM

inferredcites →https://doi.org/10.1109/icra19839.2013openalex-snapshot

2013 IEEE International Conference on Robotics and Automation

inferredcites →https://doi.org/10.1109/icra40945.2020.9197155openalex-snapshot

A 3D-Deep-Learning-based Augmented Reality Calibration Method for Robotic Environments using Depth Sensor Data

inferredcites →https://doi.org/10.1109/icra40945.2020.9197385openalex-snapshot

A*3D Dataset: Towards Autonomous Driving in Challenging Environments

inferredcites →https://doi.org/10.1109/iros.2016.7759630openalex-snapshot

Real-time height map fusion using differentiable rendering

inferredcites →https://doi.org/10.1109/lra.2018.2852843openalex-snapshot

RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

inferredcites →https://doi.org/10.1109/mra.2020.2977290openalex-snapshot

Deep Learning-Based Localization and Perception Systems: Approaches for Autonomous Cargo Transportation Vehicles in Large-Scale, Semiclosed Environments

inferredcites →https://doi.org/10.1109/tcst.2016.2518618openalex-snapshot

Formation Control of Mobile Robots Using Distributed Controller With Sampled-Data and Communication Delays

inferredcites →https://doi.org/10.1109/tvcg.2003.1175093openalex-snapshot

Computing and rendering point set surfaces

inferredcites →https://doi.org/10.1109/tvcg.2021.3058311openalex-snapshot

Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud

inferredcites →https://doi.org/10.1145/1275808.1276405openalex-snapshot

Parameterization-free projection for geometry reconstruction

inferredcites →https://doi.org/10.1145/1276377.1276405openalex-snapshot

Parameterization-free projection for geometry reconstruction

inferredcites →https://doi.org/10.1145/2421636.2421645openalex-snapshot

Edge-aware point set resampling

inferredcites →https://doi.org/10.1145/2816795.2818073openalex-snapshot

Deep points consolidation

inferredcites →https://doi.org/10.1145/3145690.3145729openalex-snapshot

Visualization and labeling of point clouds in virtual reality

inferredcites →https://doi.org/10.1145/3197517.3201322openalex-snapshot

Perception-aware modeling and fabrication of digital drawing tools

inferredcites →https://doi.org/10.1145/3197517.3201378openalex-snapshot

Single-image SVBRDF capture with a rendering-aware deep network

inferredcites →https://doi.org/10.1145/3272127.3275109openalex-snapshot

Differentiable Monte Carlo ray tracing through edge sampling

inferredcites →https://doi.org/10.1145/3319535.3339815openalex-snapshot

Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving

inferredcites →https://doi.org/10.1145/3326362openalex-snapshot

Dynamic Graph CNN for Learning on Point Clouds

inferredcites →https://doi.org/10.1177/0278364913491297openalex-snapshot

Vision meets robotics: The KITTI dataset

inferredcites →https://doi.org/10.1177/0278364916679498openalex-snapshot

1 year, 1000 km: The Oxford RobotCar dataset

inferredcites →https://doi.org/10.3390/robotics9020046openalex-snapshot

Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation

inferredcites →https://doi.org/10.3390/s18041284openalex-snapshot

Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4650262openalex-extracted

Spu-Emvs: Self-Supervised Point Cloud Upsampling Based on Edge-Aware Multiple Views

inferred← citeshttps://doi.org/10.1007/978-3-031-26319-4_20openalex-snapshot

PU-Transformer: Point Cloud Upsampling Transformer

inferred← citeshttps://doi.org/10.1007/978-3-031-26319-4_7openalex-snapshot

Point Cloud Upsampling via Cascaded Refinement Network

inferred← citeshttps://doi.org/10.1007/978-981-97-5600-1_32openalex-snapshot

Sparse Point Cloud Upsampling Based on Neural Implicit Functions

inferred← citeshttps://doi.org/10.1007/s00530-025-01870-xopenalex-snapshot

Unsupervised arbitrary-scale point cloud upsampling by learning neural gradient function

inferred← citeshttps://doi.org/10.1007/s11042-023-16525-0openalex-snapshot

PSR-GAT: Arbitrary point cloud super-resolution using graph attention networks

inferred← citeshttps://doi.org/10.1016/j.cag.2023.07.037openalex-snapshot

UPU-SNet: Siamese network for unsupervised point cloud upsampling based on spatial-aware transformers

inferred← citeshttps://doi.org/10.1016/j.cviu.2023.103890openalex-snapshot

S 3 U-PVNet: Arbitrary-scale point cloud upsampling via Point-Voxel Network based on Siamese Self-Supervised Learning

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00204openalex-snapshot

Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01808openalex-snapshot

Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.01454openalex-snapshot

TULIP: Transformer for Upsampling of LiDAR Point Clouds

inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10888638openalex-snapshot

SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

inferred← citeshttps://doi.org/10.1109/iccc56324.2022.10065731openalex-snapshot

UPU-DGTNet: Dynamic Graph Transformer Network for Unsupervised Point Cloud Upsampling

inferred← citeshttps://doi.org/10.1109/iccv51070.2023.01320openalex-snapshot

P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds

inferred← citeshttps://doi.org/10.1109/icip49359.2023.10223131openalex-snapshot

Point Cloud Upsampling with Dynamic Graph Scattering Transform

inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11227870openalex-snapshot

Efficiency-Optimized Point Cloud Upsampling with Single-Layer Graph Convolution Network

inferred← citeshttps://doi.org/10.1109/lsp.2025.3631424openalex-snapshot

Contrastive Attention-Based Network for Self-Supervised Point Cloud Completion

inferred← citeshttps://doi.org/10.1109/tim.2022.3227994openalex-snapshot

Multi-Dimensional Graph Interactional Network for Progressive Point Cloud Completion

inferred← citeshttps://doi.org/10.1109/tits.2024.3388276openalex-snapshot

PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface

inferred← citeshttps://doi.org/10.1109/tpami.2023.3287628openalex-snapshot

Self-Supervised Arbitrary-Scale Implicit Point Clouds Upsampling

inferred← citeshttps://doi.org/10.1109/vcip63160.2024.10849879openalex-snapshot

PointCU: Multiscale Sparse Convolutional Learning for Point Cloud Color Upsampling

inferred← citeshttps://doi.org/10.1145/3503161.3547777openalex-snapshot

PC 2 -PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling

inferred← citeshttps://doi.org/10.1145/3552457.3555728openalex-snapshot

IPDAE

inferred← citeshttps://doi.org/10.1145/3581783.3612048openalex-snapshot

CPU: Codebook Lookup Transformer with Knowledge Distillation for Point Cloud Upsampling

inferred← citeshttps://doi.org/10.1145/3672608.3707900openalex-snapshot

PULLM: A Multimodal Framework for Enhanced 3D Point Cloud Upsampling Using Large Language Models

inferred← citeshttps://doi.org/10.11834/jig.221076openalex-snapshot

Deep learning-based quality enhancement for 3D point clouds：a survey

inferred← citeshttps://doi.org/10.12677/airr.2025.142028openalex-snapshot

Structure-Aware Self-Supervised Point Cloud Upsampling Network

inferred← citeshttps://doi.org/10.12677/jisp.2024.131002openalex-snapshot

Research on Point Cloud Upsampling Technologies

inferred← citeshttps://doi.org/10.1609/aaai.v38i2.27929openalex-snapshot

Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning

inferred← citeshttps://doi.org/10.26599/cvm.2026.9450570openalex-snapshot

Deep Learning for Geometric 3D Point Cloud Enhancement: A Survey

inferred← citeshttps://doi.org/10.3389/fphy.2024.1474797openalex-snapshot

Outdoor large-scene 3D point cloud reconstruction based on transformer

inferred← citeshttps://doi.org/10.3390/app14041510openalex-snapshot

TSE-UNet: Temporal and Spatial Feature-Enhanced Point Cloud Super-Resolution Model for Mechanical LiDAR

inferred← citeshttps://doi.org/10.3390/app14156814openalex-snapshot

RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction

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