3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning

• URL: http://arxiv.org/abs/2103.14533v1
• Date: Fri, 26 Mar 2021 15:38:33 GMT
• Title: 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning
• Authors: Sofiane Horache and Jean-Emmanuel Deschaud and Fran\c{c}ois Goulette
• Abstract summary: MS-SVConv is a fast multi-scale deep neural network that outputs features from point clouds for 3D registration between two scenes. We show significant improvements compared to state-of-the-art methods on the competitive and well-known 3DMatch benchmark. We present a strategy to fine-tune MS-SVConv on unknown datasets in a self-supervised way, which leads to state-of-the-art results on ETH and TUM datasets.
• Score: 5.629161809575013
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present MS-SVConv, a fast multi-scale deep neural network that outputs features from point clouds for 3D registration between two scenes. We compute features using a 3D sparse voxel convolutional network on a point cloud at different scales and then fuse the features through fully-connected layers. With supervised learning, we show significant improvements compared to state-of-the-art methods on the competitive and well-known 3DMatch benchmark. We also achieve a better generalization through different source and target datasets, with very fast computation. Finally, we present a strategy to fine-tune MS-SVConv on unknown datasets in a self-supervised way, which leads to state-of-the-art results on ETH and TUM datasets.

Related papers

• fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence [50.417261057533786]
  fVDB is a novel framework for deep learning on large-scale 3D data. Our framework is fully integrated with PyTorch enabling interoperability with existing pipelines.
  arXiv  Detail & Related papers  (2024-07-01T20:20:33Z)
• 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)
• SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation [53.83313235792596]
  We present a new methodology for real-time semantic mapping from RGB-D sequences. It combines a 2D neural network and a 3D network based on a SLAM system with 3D occupancy mapping. Our system achieves state-of-the-art semantic mapping quality within 2D-3D networks-based systems.
  arXiv  Detail & Related papers  (2023-06-28T22:36:44Z)
• M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers [78.48081972698888]
  We present M3DeTR, which combines different point cloud representations with different feature scales based on multi-scale feature pyramids. M3DeTR is the first approach that unifies multiple point cloud representations, feature scales, as well as models mutual relationships between point clouds simultaneously using transformers.
  arXiv  Detail & Related papers  (2021-04-24T06:48:23Z)
• H3D: Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and textured Meshes from UAV LiDAR and Multi-View-Stereo [4.263987603222371]
  This paper introduces a 3D dataset which is unique in three ways. It depicts the village of Hessigheim (Germany) henceforth referred to as H3D. It is designed for promoting research in the field of 3D data analysis on one hand and to evaluate and rank emerging approaches.
  arXiv  Detail & Related papers  (2021-02-10T09:33:48Z)
• The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions [15.997907568429177]
  This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes. We derive a novel viewpoint-invariant descriptor by utilizing 3D geometric properties as the input to PointConv. Experiments are conducted on the 2D MNIST & CIFAR-10 datasets as well as the 3D Semantic KITTI & ScanNet dataset.
  arXiv  Detail & Related papers  (2021-01-19T19:32:38Z)
• S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds [0.16799377888527683]
  We present S3CNet, a sparse convolution based neural network that predicts the semantically completed scene from a single, unified LiDAR point cloud. We show that our proposed method outperforms all counterparts on the 3D task, achieving state-of-the art results on the Semantic KITTI benchmark.
  arXiv  Detail & Related papers  (2020-12-16T20:14:41Z)
• DV-ConvNet: Fully Convolutional Deep Learning on Point Clouds with Dynamic Voxelization and 3D Group Convolution [0.7340017786387767]
  3D point cloud interpretation is a challenging task due to the randomness and sparsity of the component points. In this work, we draw attention back to the standard 3D convolutions towards an efficient 3D point cloud interpretation. Our network is able to run and converge at a considerably fast speed, while yields on-par or even better performance compared with the state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-07T07:45:05Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)
• D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [51.04841465193678]
  We leverage a 3D fully convolutional network for 3D point clouds. We propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point. Our method achieves state-of-the-art results in both indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2020-03-06T12:51:09Z)

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.