mlsanityData to 7 Oct 2026

GRJointNET: Synergistic Completion and Part Segmentation on 3D Incomplete Point Clouds

arXiv 2311.13997 ↗First posted 23 Nov 2023cs.CVcited by 0

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Abstract (arXiv)

Segmentation of three-dimensional (3D) point clouds is an important task for autonomous systems. However, success of segmentation algorithms depends greatly on the quality of the underlying point clouds (resolution, completeness etc.). In particular, incomplete point clouds might reduce a downstream model's performance. GRNet is proposed as a novel and recent deep learning solution to complete point clouds, but it is not capable of part segmentation. On the other hand, our proposed solution, GRJointNet, is an architecture that can perform joint completion and segmentation on point clouds as a successor of GRNet. Features extracted for the two tasks are also utilized by each other to increase the overall performance. We evaluated our proposed network on the ShapeNet-Part dataset and compared its performance to GRNet. Our results demonstrate GRJointNet can outperform GRNet on point completion. It should also be noted that GRNet is not capable of segmentation while GRJointNet is. This study1, therefore, holds a promise to enhance practicality and utility of point clouds in 3D vision for autonomous systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 23 Nov 2023.

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18 of 27 references.

References · 18

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  3. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  4. 2016Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesiscited by 219
  5. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  6. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
  7. 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
  8. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 166
  9. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  10. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  11. 2019Morphing and Sampling Network for Dense Point Cloud Completioncited by 132
  12. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
6 more
  1. 2017High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inferencecited by 87
  2. 2021PTTR: Relational 3D Point Cloud Object Tracking with Transformercited by 65
  3. 2022PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Pathscited by 57
  4. 2022Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformercited by 38
  5. 2018VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapescited by 37
  6. 2020SyNet: An Ensemble Network for Object Detection in UAV Imagescited by 9

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