Lidar Upsampling with Sliced Wasserstein Distance

• URL: http://arxiv.org/abs/2301.13558v1
• Date: Tue, 31 Jan 2023 11:16:21 GMT
• Title: Lidar Upsampling with Sliced Wasserstein Distance
• Authors: Artem Savkin, and Yida Wang, Sebastian Wirkert, and Nassir Navab, and Federico Tombar
• Abstract summary: We propose a method for lidar point cloud upsampling which can reconstruct fine-grained lidar scan patterns. The key idea is to utilize edge-aware dense convolutions for both feature extraction and feature expansion. This in turn enables our method to employ a one-stage upsampling paradigm without the need for coarse and fine reconstruction.
• Score: 38.79696730246419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lidar became an important component of the perception systems in autonomous driving. But challenges of training data acquisition and annotation made emphasized the role of the sensor to sensor domain adaptation. In this work, we address the problem of lidar upsampling. Learning on lidar point clouds is rather a challenging task due to their irregular and sparse structure. Here we propose a method for lidar point cloud upsampling which can reconstruct fine-grained lidar scan patterns. The key idea is to utilize edge-aware dense convolutions for both feature extraction and feature expansion. Additionally applying a more accurate Sliced Wasserstein Distance facilitates learning of the fine lidar sweep structures. This in turn enables our method to employ a one-stage upsampling paradigm without the need for coarse and fine reconstruction. We conduct several experiments to evaluate our method and demonstrate that it provides better upsampling.

Related papers

• Enhancing Sampling Protocol for Robust Point Cloud Classification [7.6224558218559855]
  Real-world data often suffer from corrputions such as sensor noise, which violates the benignness assumption of point cloud in current protocols. We propose an enhanced point cloud sampling protocol, PointDR, which comprises two components: 1) Downsampling for key point identification and 2) Resampling for flexible sample size.
  arXiv  Detail & Related papers  (2024-08-22T01:48:31Z)
• Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions [77.32043242988738]
  We propose a new framework for accurate point cloud upsampling that supports arbitrary upsampling rates. Our method first interpolates the low-res point cloud according to a given upsampling rate.
  arXiv  Detail & Related papers  (2023-04-24T06:36:35Z)
• BIMS-PU: Bi-Directional and Multi-Scale Point Cloud Upsampling [60.257912103351394]
  We develop a new point cloud upsampling pipeline called BIMS-PU. We decompose the up/downsampling procedure into several up/downsampling sub-steps by breaking the target sampling factor into smaller factors. We show that our method achieves superior results to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-25T13:13:37Z)
• Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation [79.60988242843437]
  We propose a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than supervised learning based state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-18T07:18:25Z)
• PC2-PU: Patch Correlation and Position Correction for Effective Point Cloud Upsampling [12.070762117164092]
  Point cloud upsampling is to densify a sparse point set acquired from 3D sensors. Existing methods perform upsampling on a single patch, ignoring the coherence and relation of the entire surface. We present a novel method for more effective point cloud upsampling, achieving a more robust and improved performance.
  arXiv  Detail & Related papers  (2021-09-20T07:40:20Z)
• SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization [52.20602782690776]
  It is expensive and tedious to obtain large scale paired sparse-canned point sets for training from real scanned sparse data. We propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performance to the state-of-the-art supervised methods.
  arXiv  Detail & Related papers  (2020-12-08T14:14:09Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)

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.