4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds

• URL: http://arxiv.org/abs/2209.07121v1
• Date: Thu, 15 Sep 2022 08:05:42 GMT
• Title: 4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds
• Authors: Alvari Sepp\"anen, Risto Ojala, Kari Tammi
• Abstract summary: This letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet) Our algorithm takes advantage of the time dimension unlike deep learning adverse weather denoising methods in the literature. Results are achieved on our novel Snowy KITTI dataset, which has over 40000 adverse weather annotated point clouds.
• Score: 1.8563342761346613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable point cloud data is essential for perception tasks \textit{e.g.} in robotics and autonomous driving applications. Adverse weather causes a specific type of noise to light detection and ranging (LiDAR) sensor data, which degrades the quality of the point clouds significantly. To address this issue, this letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time dimension unlike deep learning adverse weather denoising methods in the literature. It performs about 10\% better in terms of intersection over union metric compared to the previous work and is more computationally efficient. These results are achieved on our novel SnowyKITTI dataset, which has over 40000 adverse weather annotated point clouds. Moreover, strong qualitative results on the Canadian Adverse Driving Conditions dataset indicate good generalizability to domain shifts and to different sensor intrinsics.

Related papers

• Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors [53.6277160912059]
  We propose a method to promote pros of data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs. We introduce a novel statistical reasoning algorithm in local regions which is able to finetune data-driven based priors without signed distance supervision, clean point cloud, or point normals.
  arXiv  Detail & Related papers  (2024-10-25T16:48:44Z)
• TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather [6.752848431431841]
  Real-world adverse weather conditions, such as rain, fog, and snow, introduce significant noise and interference. Existing datasets often suffer from limited weather diversity and small dataset sizes. We propose a novel point cloud denoising model, TripleMixer, comprising three mixer layers.
  arXiv  Detail & Related papers  (2024-08-25T10:45:52Z)
• Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mapping [54.38209327518066]
  Learning signed distance functions from point clouds is an important task in 3D computer vision. We propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision. Our novelty lies in the noise to noise mapping which can infer a highly accurate SDF of a single object or scene from its multiple or even single noisy observations.
  arXiv  Detail & Related papers  (2024-07-04T03:35:02Z)
• Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions [10.306226508237348]
  Adverse weather conditions can severely affect the performance of LiDAR sensors. Current approaches for detecting adverse weather points require large amounts of labeled data. This paper proposes a label-efficient approach to segment LiDAR point clouds in adverse weather.
  arXiv  Detail & Related papers  (2024-06-14T10:29:00Z)
• Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping [52.25114448281418]
  Learning signed distance functions (SDFs) from 3D point clouds is an important task in 3D computer vision. We propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision for training. Our novelty lies in the noise to noise mapping which can infer a highly accurate SDF of a single object or scene from its multiple or even single noisy point cloud observations.
  arXiv  Detail & Related papers  (2023-06-02T09:52:04Z)
• Energy-based Detection of Adverse Weather Effects in LiDAR Data [7.924836086640871]
  We propose a novel approach for detecting adverse weather effects in LiDAR data. Our method learns to associate low energy scores with inlier points and high energy scores with outliers. To help expand the research field of LiDAR perception in adverse weather, we release the SemanticSpray dataset.
  arXiv  Detail & Related papers  (2023-05-25T15:03:36Z)
• Multi-Echo Denoising in Adverse Weather [1.8563342761346613]
  Adverse weather can cause noise to light detection and ranging (LiDAR) data. We propose the task of multi-echo denoising, where the goal is to pick the echo that represents the objects of interest and discard other echoes. We propose a novel self-supervised deep learning method and the characteristics similarity regularization method to boost its performance.
  arXiv  Detail & Related papers  (2023-05-23T12:40:28Z)
• LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud [28.738804719418088]
  We introduce an unsupervised de-noising algorithm, LiSnowNet, running 52$times$ faster than the state-of-the-art methods. Unlike previous methods, the proposed algorithm is based on a deep convolutional neural network and can be easily deployed to hardware accelerators.
  arXiv  Detail & Related papers  (2022-11-18T04:19:05Z)
• Shape-invariant 3D Adversarial Point Clouds [111.72163188681807]
  Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been criticized for their noticeable point outliers. We propose a novel Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility of point perturbations.
  arXiv  Detail & Related papers  (2022-03-08T12:21:35Z)
• Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey [104.71816962689296]
  Unsupervised point cloud representation learning has attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using deep neural networks.
  arXiv  Detail & Related papers  (2022-02-28T07:46:05Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)

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.