Point-Voxel Transformer: An Efficient Approach To 3D Deep Learning

• URL: http://arxiv.org/abs/2108.06076v1
• Date: Fri, 13 Aug 2021 06:07:57 GMT
• Title: Point-Voxel Transformer: An Efficient Approach To 3D Deep Learning
• Authors: Cheng Zhang, Haocheng Wan, Shengqiang Liu, Xinyi Shen, Zizhao Wu
• Abstract summary: We present a novel 3D Transformer, called Point-Voxel Transformer (PVT) that leverages self-attention computation in points to gather global context features. Our method fully exploits the potentials of Transformer architecture, paving the road to efficient and accurate recognition results.
• Score: 5.236787242129767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the sparsity and irregularity of the 3D data, approaches that directly process points have become popular. Among all point-based models, Transformer-based models have achieved state-of-the-art performance by fully preserving point interrelation. However, most of them spend high percentage of total time on sparse data accessing (e.g., Farthest Point Sampling (FPS) and neighbor points query), which becomes the computation burden. Therefore, we present a novel 3D Transformer, called Point-Voxel Transformer (PVT) that leverages self-attention computation in points to gather global context features, while performing multi-head self-attention (MSA) computation in voxels to capture local information and reduce the irregular data access. Additionally, to further reduce the cost of MSA computation, we design a cyclic shifted boxing scheme which brings greater efficiency by limiting the MSA computation to non-overlapping local boxes while also preserving cross-box connection. Our method fully exploits the potentials of Transformer architecture, paving the road to efficient and accurate recognition results. Evaluated on classification and segmentation benchmarks, our PVT not only achieves strong accuracy but outperforms previous state-of-the-art Transformer-based models with 9x measured speedup on average. For 3D object detection task, we replace the primitives in Frustrum PointNet with PVT layer and achieve the improvement of 8.6%.

Related papers

• PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture [46.266960248570086]
  This study tackles the quadratic complexity of the self-attention mechanism by introducing a complexity local attention mechanism for effective feature aggregation. We also introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel. We show that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy.
  arXiv  Detail & Related papers  (2024-08-10T10:16:03Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer [75.2251801053839]
  We present a novel Point-Voxel Transformer for single-stage 3D detection (PVT-SSD) We propose a Point-Voxel Transformer (PVT) module that obtains long-range contexts in a cheap manner from voxels. The experiments on several autonomous driving benchmarks verify the effectiveness and efficiency of the proposed method.
  arXiv  Detail & Related papers  (2023-05-11T07:37:15Z)
• CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning [81.85951026033787]
  We set transformers in this work and incorporate them into a hierarchical framework for shape classification and part and scene segmentation. We also compute efficient and dynamic global cross attentions by leveraging sampling and grouping at each iteration. The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods.
  arXiv  Detail & Related papers  (2022-07-31T21:39:15Z)
• Stratified Transformer for 3D Point Cloud Segmentation [89.9698499437732]
  Stratified Transformer is able to capture long-range contexts and demonstrates strong generalization ability and high performance. To combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information. Experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets.
  arXiv  Detail & Related papers  (2022-03-28T05:35:16Z)
• LocATe: End-to-end Localization of Actions in 3D with Transformers [91.28982770522329]
  LocATe is an end-to-end approach that jointly localizes and recognizes actions in a 3D sequence. Unlike transformer-based object-detection and classification models which consider image or patch features as input, LocATe's transformer model is capable of capturing long-term correlations between actions in a sequence. We introduce a new, challenging, and more realistic benchmark dataset, BABEL-TAL-20 (BT20), where the performance of state-of-the-art methods is significantly worse.
  arXiv  Detail & Related papers  (2022-03-21T03:35:32Z)
• Fast Point Transformer [39.96609666253924]
  This paper introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates, and the voxel hashing-based architecture boosts computational efficiency. The accuracy of our approach is competitive to the best voxel-based method, and our network achieves 136 times faster inference time than the state-of-the-art, Point Transformer.
  arXiv  Detail & Related papers  (2021-12-09T05:04:10Z)
• CpT: Convolutional Point Transformer for 3D Point Cloud Processing [10.389972581905]
  We present CpT: Convolutional point Transformer - a novel deep learning architecture for dealing with the unstructured nature of 3D point cloud data. CpT is an improvement over existing attention-based Convolutions Neural Networks as well as previous 3D point cloud processing transformers. Our model can serve as an effective backbone for various point cloud processing tasks when compared to the existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-21T17:45:55Z)
• 3DSSD: Point-based 3D Single Stage Object Detector [61.67928229961813]
  We present a point-based 3D single stage object detector, named 3DSSD, achieving a good balance between accuracy and efficiency. Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well.
  arXiv  Detail & Related papers  (2020-02-24T12:01:58Z)

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.