Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data

• URL: http://arxiv.org/abs/2309.02139v2
• Date: Fri, 22 Dec 2023 11:56:53 GMT
• Title: Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data
• Authors: Mariona Car\'os, Ariadna Just, Santi Segu\'i, Jordi Vitri\`a
• Abstract summary: We propose to train a self-supervised encoder with Barlow Twins and use it as a pre-trained network in the task of semantic scene segmentation. The experimental results demonstrate that our unsupervised pre-training boosts performance once fine-tuned on the supervised task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Airborne LiDAR systems have the capability to capture the Earth's surface by generating extensive point cloud data comprised of points mainly defined by 3D coordinates. However, labeling such points for supervised learning tasks is time-consuming. As a result, there is a need to investigate techniques that can learn from unlabeled data to significantly reduce the number of annotated samples. In this work, we propose to train a self-supervised encoder with Barlow Twins and use it as a pre-trained network in the task of semantic scene segmentation. The experimental results demonstrate that our unsupervised pre-training boosts performance once fine-tuned on the supervised task, especially for under-represented categories.

Related papers

• SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations [76.45009891152178]
  Pretraining-finetuning approach can alleviate the labeling burden by fine-tuning a pre-trained backbone across various downstream datasets as well as tasks. We show, for the first time, that general representations learning can be achieved through the task of occupancy prediction. Our findings will facilitate the understanding of LiDAR points and pave the way for future advancements in LiDAR pre-training.
  arXiv  Detail & Related papers  (2023-09-19T11:13:01Z)
• MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds [13.426810473131642]
  Masked AutoEncoder for LiDAR point clouds (MAELi) intuitively leverages the sparsity of LiDAR point clouds in both the encoder and decoder during reconstruction. In a novel reconstruction approach, MAELi distinguishes between empty and occluded space. Thereby, without any ground truth whatsoever and trained on single frames only, MAELi obtains an understanding of the underlying 3D scene geometry and semantics.
  arXiv  Detail & Related papers  (2022-12-14T13:10:27Z)
• LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [62.49198183539889]
  We propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning. Our proposed method is even highly competitive compared to the fully supervised counterpart with 100% labels.
  arXiv  Detail & Related papers  (2022-10-14T19:13:36Z)
• 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone [10.341296683155973]
  We propose using a self-supervised training strategy to learn a general point cloud backbone model for downstream 3D vision tasks. Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a 3D detection head. Experiments on KITTI and nuScenes benchmarks show that the proposed self-supervised pre-training increases 3D detection performance significantly.
  arXiv  Detail & Related papers  (2022-05-02T07:53:29Z)
• Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation [90.2445084743881]
  We present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance. Inspired by the recent contrastive loss in self-supervised tasks, we propose the guided point contrastive loss to enhance the feature representation and model generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T16:38:54Z)
• Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds [59.63231842439687]
  We train a semantic point cloud segmentation network with only a small portion of points being labeled. We propose a cross-sample feature reallocating module to transfer similar features and therefore re-route the gradients across two samples. Our weakly supervised method with only 10% and 1% of labels can produce compatible results with the fully supervised counterpart.
  arXiv  Detail & Related papers  (2021-07-23T14:34:57Z)
• 3D Spatial Recognition without Spatially Labeled 3D [127.6254240158249]
  We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition. We show that WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time.
  arXiv  Detail & Related papers  (2021-05-13T17:58:07Z)
• PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding [107.02479689909164]
  In this work, we aim at facilitating research on 3D representation learning. We measure the effect of unsupervised pre-training on a large source set of 3D scenes.
  arXiv  Detail & Related papers  (2020-07-21T17:59:22Z)

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.