Point-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Clouds

• URL: http://arxiv.org/abs/2210.17009v1
• Date: Mon, 31 Oct 2022 01:53:51 GMT
• Title: Point-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Clouds
• Authors: Ziwei Wang, Reza Arablouei, Jiajun Liu, Paulo Borges, Greg Bishop-Hurley, Nicholas Heaney
• Abstract summary: Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving. We propose a semi-supervised cross-domain learning approach that does not rely on manual annotations of point clouds. We introduce Point-Syn2Real, a new benchmark dataset for cross-domain learning on point clouds.
• Score: 14.056949618464394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving. However, labeling point cloud data is labor-intensive as it requires human annotators to visualize and inspect the 3D data from different perspectives. In this paper, we propose a semi-supervised cross-domain learning approach that does not rely on manual annotations of point clouds and performs similar to fully-supervised approaches. We utilize available 3D object models to train classifiers that can generalize to real-world point clouds. We simulate the acquisition of point clouds by sampling 3D object models from multiple viewpoints and with arbitrary partial occlusions. We then augment the resulting set of point clouds through random rotations and adding Gaussian noise to better emulate the real-world scenarios. We then train point cloud encoding models, e.g., DGCNN, PointNet++, on the synthesized and augmented datasets and evaluate their cross-domain classification performance on corresponding real-world datasets. We also introduce Point-Syn2Real, a new benchmark dataset for cross-domain learning on point clouds. The results of our extensive experiments with this dataset demonstrate that the proposed cross-domain learning approach for point clouds outperforms the related baseline and state-of-the-art approaches in both indoor and outdoor settings in terms of cross-domain generalizability. The code and data will be available upon publishing.

Related papers

• BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation [2.446672595462589]
  We introduce the BelHouse3D dataset, a new synthetic point cloud dataset designed for 3D indoor scene semantic segmentation. This dataset is constructed using real-world references from 32 houses in Belgium, ensuring that the synthetic data closely aligns with real-world conditions.
  arXiv  Detail & Related papers  (2024-11-20T12:09:43Z)
• PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training [90.06520673092702]
  We present PointRegGPT, boosting 3D point cloud registration using generative point-cloud pairs for training. To our knowledge, this is the first generative approach that explores realistic data generation for indoor point cloud registration.
  arXiv  Detail & Related papers  (2024-07-19T06:29:57Z)
• Zero-shot Point Cloud Completion Via 2D Priors [52.72867922938023]
  3D point cloud completion is designed to recover complete shapes from partially observed point clouds. We propose a zero-shot framework aimed at completing partially observed point clouds across any unseen categories.
  arXiv  Detail & Related papers  (2024-04-10T08:02:17Z)
• U3DS$^3$: Unsupervised 3D Semantic Scene Segmentation [19.706172244951116]
  This paper presents U3DS$3$, as a step towards completely unsupervised point cloud segmentation for any holistic 3D scenes. The initial step of our proposed approach involves generating superpoints based on the geometric characteristics of each scene. We then undergo a learning process through a spatial clustering-based methodology, followed by iterative training using pseudo-labels generated in accordance with the cluster centroids.
  arXiv  Detail & Related papers  (2023-11-10T12:05:35Z)
• AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration [69.21282992341007]
  Auto Synth automatically generates 3D training data for point cloud registration. We replace the point cloud registration network with a much smaller surrogate network, leading to a $4056.43$ speedup. Our results on TUD-L, LINEMOD and Occluded-LINEMOD evidence that a neural network trained on our searched dataset yields consistently better performance than the same one trained on the widely used ModelNet40 dataset.
  arXiv  Detail & Related papers  (2023-09-20T09:29:44Z)
• Variational Relational Point Completion Network for Robust 3D Classification [59.80993960827833]
  Vari point cloud completion methods tend to generate global shape skeletons hence lack fine local details. This paper proposes a variational framework, point Completion Network (VRCNet) with two appealing properties. VRCNet shows great generalizability and robustness on real-world point cloud scans.
  arXiv  Detail & Related papers  (2023-04-18T17:03:20Z)
• Point2Vec for Self-Supervised Representation Learning on Point Clouds [66.53955515020053]
  We extend data2vec to the point cloud domain and report encouraging results on several downstream tasks. We propose point2vec, which unleashes the full potential of data2vec-like pre-training on point clouds.
  arXiv  Detail & Related papers  (2023-03-29T10:08:29Z)
• Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud Analysis [33.31864436614945]
  We propose a novel pre-training method for 3D point cloud models. Our pre-training is self-supervised by a local pixel/point level correspondence loss and a global image/point cloud level loss. These improved models outperform existing state-of-the-art methods on various datasets and downstream tasks.
  arXiv  Detail & Related papers  (2022-10-28T05:23:03Z)
• CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding [2.8661021832561757]
  CrossPoint is a simple cross-modal contrastive learning approach to learn transferable 3D point cloud representations. Our approach outperforms the previous unsupervised learning methods on a diverse range of downstream tasks including 3D object classification and segmentation.
  arXiv  Detail & Related papers  (2022-03-01T18:59:01Z)
• Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views [70.1586005070678]
  We present a system for automatically converting 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects. Our method significantly outperforms previous work despite the fact that those methods use significantly more complex pipelines, 3D models and additional human-annotated external sources of prior information.
  arXiv  Detail & Related papers  (2021-09-16T13:01:13Z)
• PnP-3D: A Plug-and-Play for 3D Point Clouds [38.05362492645094]
  We propose a plug-and-play module, -3D, to improve the effectiveness of existing networks in analyzing point cloud data. To thoroughly evaluate our approach, we conduct experiments on three standard point cloud analysis tasks. In addition to achieving state-of-the-art results, we present comprehensive studies to demonstrate our approach's advantages.
  arXiv  Detail & Related papers  (2021-08-16T23:59:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.