BiPointNet: Binary Neural Network for Point Clouds

• URL: http://arxiv.org/abs/2010.05501v4
• Date: Fri, 11 Jun 2021 15:03:56 GMT
• Title: BiPointNet: Binary Neural Network for Point Clouds
• Authors: Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Liu, Hao Su
• Abstract summary: BiPointNet is the first model binarization approach for efficient deep learning on point clouds. We show that BiPointNet gives an impressive 14.7x speedup and 18.9x storage saving on real-world resource-constrained devices.
• Score: 73.07852523426224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds. We discover that the immense performance drop of binarized models for point clouds mainly stems from two challenges: aggregation-induced feature homogenization that leads to a degradation of information entropy, and scale distortion that hinders optimization and invalidates scale-sensitive structures. With theoretical justifications and in-depth analysis, our BiPointNet introduces Entropy-Maximizing Aggregation (EMA) to modulate the distribution before aggregation for the maximum information entropy, and Layer-wise Scale Recovery (LSR) to efficiently restore feature representation capacity. Extensive experiments show that BiPointNet outperforms existing binarization methods by convincing margins, at the level even comparable with the full precision counterpart. We highlight that our techniques are generic, guaranteeing significant improvements on various fundamental tasks and mainstream backbones. Moreover, BiPointNet gives an impressive 14.7x speedup and 18.9x storage saving on real-world resource-constrained devices.

Related papers

• FBPT: A Fully Binary Point Transformer [12.373066597900127]
  This paper presents a novel Fully Binary Point Cloud Transformer (FBPT) model which has the potential to be widely applied and expanded in the fields of robotics and mobile devices. By compressing the weights and activations of a 32-bit full-precision network to 1-bit binary values, the proposed binary point cloud Transformer network significantly reduces the storage footprint and computational resource requirements. The primary focus of this paper is on addressing the performance degradation issue caused by the use of binary point cloud Transformer modules.
  arXiv  Detail & Related papers  (2024-03-15T03:45:10Z)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity [0.0]
  Exploiting sparsity in the network's feature maps is one of the ways to reduce its inference latency. We propose a solution to induce semi-structured activation sparsity exploitable through minor runtime modifications. Our approach yields a speed improvement of $1.25 times$ with a minimal accuracy drop of $1.1%$ for the ResNet18 model on the ImageNet dataset.
  arXiv  Detail & Related papers  (2023-09-12T22:28:53Z)
• BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance [54.214426436283134]
  Deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications. We present a strong yet efficient binary neural network for KWS, namely BiFSMNv2, pushing it to the real-network accuracy performance. We highlight that benefiting from the compact architecture and optimized hardware kernel, BiFSMNv2 can achieve an impressive 25.1x speedup and 20.2x storage-saving on edge hardware.
  arXiv  Detail & Related papers  (2022-11-13T18:31:45Z)
• BiFSMN: Binary Neural Network for Keyword Spotting [47.46397208920726]
  BiFSMN is an accurate and extreme-efficient binary neural network for KWS. We show that BiFSMN can achieve an impressive 22.3x speedup and 15.5x storage-saving on real-world edge hardware.
  arXiv  Detail & Related papers  (2022-02-14T05:16:53Z)
• POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing [53.74076015905961]
  We introduce point-wise operations based on Expectation-Maximization into BNNs for efficient point cloud processing. Our POEM surpasses existing the state-of-the-art binary point cloud networks by a significant margin, up to 6.7 %.
  arXiv  Detail & Related papers  (2021-11-26T09:45:01Z)
• Distribution-sensitive Information Retention for Accurate Binary Neural Network [49.971345958676196]
  We present a novel Distribution-sensitive Information Retention Network (DIR-Net) to retain the information of the forward activations and backward gradients. Our DIR-Net consistently outperforms the SOTA binarization approaches under mainstream and compact architectures. We conduct our DIR-Net on real-world resource-limited devices which achieves 11.1 times storage saving and 5.4 times speedup.
  arXiv  Detail & Related papers  (2021-09-25T10:59:39Z)
• CondenseNet V2: Sparse Feature Reactivation for Deep Networks [87.38447745642479]
  Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. We propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.
  arXiv  Detail & Related papers  (2021-04-09T14:12:43Z)
• FatNet: A Feature-attentive Network for 3D Point Cloud Processing [1.502579291513768]
  We introduce a novel feature-attentive neural network layer, a FAT layer, that combines both global point-based features and local edge-based features in order to generate better embeddings. Our architecture achieves state-of-the-art results on the task of point cloud classification, as demonstrated on the ModelNet40 dataset.
  arXiv  Detail & Related papers  (2021-04-07T23:13:56Z)

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.