Pith. sign in

REVIEW 4 cited by

Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1904.10014 v2 pith:SSO5OOCD submitted 2019-04-22 cs.CV

Kuangen Zhang , Ming Hao , Jing Wang , Clarence W. de Silva , Chenglong Fu This is my paper · ORCID
classification cs.CV
keywords cloudnetworkpointgraphdynamicldgcnnneuralconvolutional
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly. However, the point cloud is sparse, unstructured, and unordered, which cannot be recognized accurately by a traditional convolutional neural network (CNN) nor a recurrent neural network (RNN). Fortunately, a graph convolutional neural network (Graph CNN) can process sparse and unordered data. Hence, we propose a linked dynamic graph CNN (LDGCNN) to classify and segment point cloud directly in this paper. We remove the transformation network, link hierarchical features from dynamic graphs, freeze feature extractor, and retrain the classifier to increase the performance of LDGCNN. We explain our network using theoretical analysis and visualization. Through experiments, we show that the proposed LDGCNN achieves state-of-art performance on two standard datasets: ModelNet40 and ShapeNet.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Point Clouds Upsampling via Flow Matching

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A flow-matching model with Earth Mover's Distance pre-alignment upsamples point clouds in five sampling steps, with state-of-the-art Chamfer distance scores on PUGAN and PU1K.

  2. GRAMA: Adaptive Graph Autoregressive Moving Average Models

    cs.LG 2025-01 conditional novelty 6.0 of 10

    GRAMA is a graph-adaptive ARMA architecture that wraps GNN backbones with selective sequential recurrences and reports consistent gains on long-range graph benchmarks.

  3. Face recognition on point cloud with cgan-top for denoising

    cs.CV 2025-06 reject novelty 5.0 of 10

    A cGAN denoiser on three orthogonal planes plus an LDGCNN recognizer is reported to improve noisy 3D face recognition accuracy by up to 14.81% over prior methods on Bosphorus.

  4. ObitoNet: Multimodal High-Resolution Point Cloud Reconstruction

    cs.CV 2024-12 reject novelty 4.0 of 10

    ObitoNet fuses ViT image tokens and FPS/KNN point-cloud tokens with a cross-attention decoder, reporting a Chamfer loss of 1.36 on Tanks and Temples versus PointMAE's 1.53.

Pith tools