MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions

• URL: http://arxiv.org/abs/2403.07593v2
• Date: Wed, 13 Mar 2024 09:39:14 GMT
• Title: MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions
• Authors: J.J. Cabrera, A. Santo, A. Gil, C. Viegas and L. Pay\'a
• Abstract summary: MinkUNeXt is an effective and efficient architecture for place-recognition from point clouds entirely based on the new 3D MinkNeXt Block. A thorough assessment of the proposal has been carried out using the Oxford RobotCar and the In-house datasets.
• Score: 1.124958340749622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents MinkUNeXt, an effective and efficient architecture for place-recognition from point clouds entirely based on the new 3D MinkNeXt Block, a residual block composed of 3D sparse convolutions that follows the philosophy established by recent Transformers but purely using simple 3D convolutions. Feature extraction is performed at different scales by a U-Net encoder-decoder network and the feature aggregation of those features into a single descriptor is carried out by a Generalized Mean Pooling (GeM). The proposed architecture demonstrates that it is possible to surpass the current state-of-the-art by only relying on conventional 3D sparse convolutions without making use of more complex and sophisticated proposals such as Transformers, Attention-Layers or Deformable Convolutions. A thorough assessment of the proposal has been carried out using the Oxford RobotCar and the In-house datasets. As a result, MinkUNeXt proves to outperform other methods in the state-of-the-art.

Related papers

• Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers [37.14235383028582]
  We introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks, namely a point decoder and a triplane decoder, to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation.
  arXiv  Detail & Related papers  (2023-12-14T17:18:34Z)
• Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation [66.6890991207065]
  Sparse 3D convolutions have become the de-facto tools to construct deep neural networks. We propose an alternative method that reaches the level of state-of-the-art methods without requiring sparse convolutions. We show that such level of performance is achievable by relying on tools a priori unfit for large scale and high-performing 3D perception.
  arXiv  Detail & Related papers  (2023-01-24T16:10:08Z)
• CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds [55.44204039410225]
  We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels. To recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module.
  arXiv  Detail & Related papers  (2022-10-09T13:38:48Z)
• Focal Sparse Convolutional Networks for 3D Object Detection [121.45950754511021]
  We introduce two new modules to enhance the capability of Sparse CNNs. They are focal sparse convolution (Focals Conv) and its multi-modal variant of focal sparse convolution with fusion. For the first time, we show that spatially learnable sparsity in sparse convolution is essential for sophisticated 3D object detection.
  arXiv  Detail & Related papers  (2022-04-26T17:34:10Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection [39.64891219500416]
  3D object detection methods exploit either voxel-based or point-based features to represent 3D objects in a scene. We introduce in this paper a novel single-stage 3D detection method having the merit of both voxel-based and point-based features.
  arXiv  Detail & Related papers  (2021-04-02T06:34:49Z)
• Generative Sparse Detection Networks for 3D Single-shot Object Detection [43.91336826079574]
  3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality. Yet, the sparse nature of the 3D data poses unique challenges to this task. We propose Generative Sparse Detection Network (GSDN), a fully-convolutional single-shot sparse detection network.
  arXiv  Detail & Related papers  (2020-06-22T15:54:24Z)
• Convolutional Occupancy Networks [88.48287716452002]
  We propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. By combining convolutional encoders with implicit occupancy decoders, our model incorporates inductive biases, enabling structured reasoning in 3D space. We empirically find that our method enables the fine-grained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data.
  arXiv  Detail & Related papers  (2020-03-10T10:17:07Z)
• Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion [53.885984328273686]
  Implicit Feature Networks (IF-Nets) deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data. IF-Nets clearly outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions.
  arXiv  Detail & Related papers  (2020-03-03T11:14:29Z)

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.