Continuous Conditional Random Field Convolution for Point Cloud Segmentation

• URL: http://arxiv.org/abs/2110.06085v1
• Date: Tue, 12 Oct 2021 15:35:38 GMT
• Title: Continuous Conditional Random Field Convolution for Point Cloud Segmentation
• Authors: Fei Yang, Franck Davoine, Huan Wang, Zhong Jin
• Abstract summary: conditional random fields (CRFs) are usually formulated as discrete models in label space to encourage label consistency. In this paper, we reconsider the CRF in feature space for point cloud segmentation because it can capture the structure of features well. Experiments on various point cloud benchmarks demonstrate the effectiveness and robustness of the proposed method.
• Score: 12.154944192318936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud segmentation is the foundation of 3D environmental perception for modern intelligent systems. To solve this problem and image segmentation, conditional random fields (CRFs) are usually formulated as discrete models in label space to encourage label consistency, which is actually a kind of postprocessing. In this paper, we reconsider the CRF in feature space for point cloud segmentation because it can capture the structure of features well to improve the representation ability of features rather than simply smoothing. Therefore, we first model the point cloud features with a continuous quadratic energy model and formulate its solution process as a message-passing graph convolution, by which it can be easily integrated into a deep network. We theoretically demonstrate that the message passing in the graph convolution is equivalent to the mean-field approximation of a continuous CRF model. Furthermore, we build an encoder-decoder network based on the proposed continuous CRF graph convolution (CRFConv), in which the CRFConv embedded in the decoding layers can restore the details of high-level features that were lost in the encoding stage to enhance the location ability of the network, thereby benefiting segmentation. Analogous to the CRFConv, we show that the classical discrete CRF can also work collaboratively with the proposed network via another graph convolution to further improve the segmentation results. Experiments on various point cloud benchmarks demonstrate the effectiveness and robustness of the proposed method. Compared with the state-of-the-art methods, the proposed method can also achieve competitive segmentation performance.

Related papers

• Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement [19.575833741231953]
  We use the KNN method to determine the neighborhoods of raw surface points. A conditional probability model is adaptive to local geometry, leading to significant rate reduction. We incorporate an implicit neural representation into the refinement layer, allowing the decoder to sample points on the underlying surface at arbitrary densities.
  arXiv  Detail & Related papers  (2024-08-06T05:24:06Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Neural Structured Prediction for Inductive Node Classification [29.908759584092167]
  This paper studies node classification in the inductive setting, aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. We present a new approach called the Structured Proxy Network (SPN), which combines the advantages of both worlds.
  arXiv  Detail & Related papers  (2022-04-15T15:50:27Z)
• Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks [27.600081147252155]
  We develop a novel graph sumable framework to maximize energy efficiency in wireless communication networks. We show the permutation training which is a desirable property for models of wireless network data. Results demonstrate its generalizability across different network topologies.
  arXiv  Detail & Related papers  (2022-01-27T20:23:24Z)
• RSI-Net: Two-Stream Deep Neural Network Integrating GCN and Atrous CNN for Semantic Segmentation of High-resolution Remote Sensing Images [3.468780866037609]
  Two-stream deep neural network for semantic segmentation of remote sensing images (RSI-Net) is proposed in this paper. Experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets. Results demonstrate the superior performance of RSI-Net in terms of overall accuracy, F1 score and kappa coefficient when compared with six state-of-the-art RSI semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-09-19T15:57:20Z)
• Rate Distortion Characteristic Modeling for Neural Image Compression [59.25700168404325]
  End-to-end optimization capability offers neural image compression (NIC) superior lossy compression performance. distinct models are required to be trained to reach different points in the rate-distortion (R-D) space. We make efforts to formulate the essential mathematical functions to describe the R-D behavior of NIC using deep network and statistical modeling.
  arXiv  Detail & Related papers  (2021-06-24T12:23:05Z)
• Semantic Segmentation by Improved Generative Adversarial Networks [0.0]
  We introduce Convolutional CRFs (ConvCRFs) as an effective improvement solution for the image semantic segmentation task. Our method not only learns an end-to-end mapping from input image to corresponding output image, but also learns a loss function to train this mapping.
  arXiv  Detail & Related papers  (2021-04-20T11:59:29Z)
• Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction [158.88345945211185]
  We present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. We propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.
  arXiv  Detail & Related papers  (2021-01-08T04:14:29Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• Towards Efficient Scene Understanding via Squeeze Reasoning [71.1139549949694]
  We propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks.
  arXiv  Detail & Related papers  (2020-11-06T12:17:01Z)
• Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation [57.62713515497585]
  Unsupervised feedforward feature learning is proposed for joint classification and segmentation of 3D point clouds. The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner. It learns global shape features through the encoder and local point features through the encoder-decoder architecture.
  arXiv  Detail & Related papers  (2020-09-02T18:25:25Z)

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.