CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation

• URL: http://arxiv.org/abs/2207.09778v1
• Date: Wed, 20 Jul 2022 09:33:42 GMT
• Title: CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
• Authors: Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni, Nicu Sebe, Elisa Ricci, Fabio Poiesi
• Abstract summary: We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix) CoSMix consists of a two-branch symmetric network that can process labelled synthetic data (source) and real-world unlabelled point clouds (target) concurrently. We evaluate CoSMix on two large-scale datasets, showing that it outperforms state-of-the-art methods by a large margin.
• Score: 62.259239847977014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to improve model generalization for different sensors and environments. Researchers working on UDA problems in the image domain have shown that sample mixing can mitigate domain shift. We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing. CoSMix consists of a two-branch symmetric network that can process labelled synthetic data (source) and real-world unlabelled point clouds (target) concurrently. Each branch operates on one domain by mixing selected pieces of data from the other one, and by using the semantic information derived from source labels and target pseudo-labels. We evaluate CoSMix on two large-scale datasets, showing that it outperforms state-of-the-art methods by a large margin. Our code is available at https://github.com/saltoricristiano/cosmix-uda.

Related papers

• Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation [65.78246406460305]
  compositional semantic mixing represents the first unsupervised domain adaptation technique for point cloud segmentation. We present a two-branch symmetric network architecture capable of concurrently processing point clouds from a source domain (e.g. synthetic) and point clouds from a target domain (e.g. real-world)
  arXiv  Detail & Related papers  (2023-08-28T14:43:36Z)
• Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation [73.3083304858763]
  This paper systematically studies the impact of mixup under the domain adaptaive semantic segmentation task. In specific, we achieve domain mixup in two-step: cut and paste. We provide extensive ablation experiments to empirically verify our main components of the framework.
  arXiv  Detail & Related papers  (2023-03-17T05:22:44Z)
• SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud [125.9472454212909]
  We present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D) SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. Experiments show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label.
  arXiv  Detail & Related papers  (2022-12-06T09:32:44Z)
• SageMix: Saliency-Guided Mixup for Point Clouds [14.94694648742664]
  We propose SageMix, a saliency-guided Mixup for point clouds to preserve salient local structures. With PointNet++, our method achieves an accuracy gain of 2.6% and 4.0% over standard training in 3D Warehouse dataset (MN40) and ScanObjectNN, respectively.
  arXiv  Detail & Related papers  (2022-10-13T12:19:58Z)
• Exploiting Instance-based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-adaptive Action Detection [75.38704117155909]
  We propose a novel domain adaptive action detection approach and a new adaptation protocol. Self-training combined with cross-domain mixed sampling has shown remarkable performance gain in UDA context. We name our proposed framework as domain-adaptive action instance mixing (DA-AIM)
  arXiv  Detail & Related papers  (2022-09-28T22:03:25Z)
• ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation [73.14508297140652]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. We propose an effective method named Proxy-based Mixup training with label refinery ( ProxyMix) Experiments on three 2D image and one 3D point cloud object recognition benchmarks demonstrate that ProxyMix yields state-of-the-art performance for source-free UDA tasks.
  arXiv  Detail & Related papers  (2022-05-29T03:45:00Z)
• DACS: Domain Adaptation via Cross-domain Mixed Sampling [4.205692673448206]
  Unsupervised domain adaptation attempts to train on labelled data from one domain, and simultaneously learn from unlabelled data in the domain of interest. We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes images from the two domains along with the corresponding labels and pseudo-labels. We demonstrate the effectiveness of our solution by achieving state-of-the-art results for GTA5 to Cityscapes.
  arXiv  Detail & Related papers  (2020-07-17T00:43:11Z)

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.