FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation

• URL: http://arxiv.org/abs/2502.09274v1
• Date: Thu, 13 Feb 2025 12:39:26 GMT
• Title: FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation
• Authors: Bin Yang, Alexandru Paul Condurache,
• Abstract summary: 3D scene understanding is a critical yet challenging task in autonomous driving. Recent methods leverage the range-view representation to improve processing efficiency. We re-design the workflow for range-view-based LiDAR semantic segmentation.
• Score: 52.89847760590189
• License:
• Abstract: 3D scene understanding is a critical yet challenging task in autonomous driving, primarily due to the irregularity and sparsity of LiDAR data, as well as the computational demands of processing large-scale point clouds. Recent methods leverage the range-view representation to improve processing efficiency. To mitigate the performance drop caused by information loss inherent to the "many-to-one" problem, where multiple nearby 3D points are mapped to the same 2D grids and only the closest is retained, prior works tend to choose a higher azimuth resolution for range-view projection. However, this can bring the drawback of reducing the proportion of pixels that carry information and heavier computation within the network. We argue that it is not the optimal solution and show that, in contrast, decreasing the resolution is more advantageous in both efficiency and accuracy. In this work, we present a comprehensive re-design of the workflow for range-view-based LiDAR semantic segmentation. Our approach addresses data representation, augmentation, and post-processing methods for improvements. Through extensive experiments on two public datasets, we demonstrate that our pipeline significantly enhances the performance of various network architectures over their baselines, paving the way for more effective LiDAR-based perception in autonomous systems.

Related papers

• LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving [52.83707400688378]
  LargeAD is a versatile and scalable framework designed for large-scale 3D pretraining across diverse real-world driving datasets. Our framework leverages VFMs to extract semantically rich superpixels from 2D images, which are aligned with LiDAR point clouds to generate high-quality contrastive samples. Our approach delivers significant performance improvements over state-of-the-art methods in both linear probing and fine-tuning tasks for both LiDAR-based segmentation and object detection.
  arXiv  Detail & Related papers  (2025-01-07T18:59:59Z)
• OccLoff: Learning Optimized Feature Fusion for 3D Occupancy Prediction [5.285847977231642]
  3D semantic occupancy prediction is crucial for ensuring the safety in autonomous driving. Existing fusion-based occupancy methods typically involve performing a 2D-to-3D view transformation on image features. We propose OccLoff, a framework that Learns to optimize Feature Fusion for 3D occupancy prediction.
  arXiv  Detail & Related papers  (2024-11-06T06:34:27Z)
• On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR [4.606106768645647]
  3D LiDAR point cloud data is crucial for scene perception in computer vision, robotics, and autonomous driving. We present DurLAR, the first high-fidelity 128-channel 3D LiDAR dataset featuring panoramic ambient (near infrared) and reflectivity imagery. To improve the segmentation accuracy, we introduce Range-Aware Pointwise Distance Distribution (RAPiD) features and the associated RAPiD-Seg architecture.
  arXiv  Detail & Related papers  (2024-11-01T14:01:54Z)
• Rethinking Range View Representation for LiDAR Segmentation [66.73116059734788]
  "Many-to-one" mapping, semantic incoherence, and shape deformation are possible impediments against effective learning from range view projections. We present RangeFormer, a full-cycle framework comprising novel designs across network architecture, data augmentation, and post-processing. We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks.
  arXiv  Detail & Related papers  (2023-03-09T16:13:27Z)
• Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach [57.67932970472768]
  Mesh and Point Cloud simplification methods aim to reduce the complexity of 3D models while retaining visual quality and relevant salient features. We propose a fast point cloud simplification method by learning to sample salient points. The proposed method relies on a graph neural network architecture trained to select an arbitrary, user-defined, number of points from the input space and to re-arrange their positions so as to minimize the visual perception error.
  arXiv  Detail & Related papers  (2021-09-30T10:23:55Z)
• EfficientLPS: Efficient LiDAR Panoptic Segmentation [30.249379810530165]
  We present the novel Efficient LiDAR Panoptic architecture that addresses multiple challenges in segmenting LiDAR point clouds. EfficientLPS comprises of a novel shared backbone that encodes with strengthened geometric transformation modeling capacity. We benchmark our proposed model on two large-scale LiDAR datasets.
  arXiv  Detail & Related papers  (2021-02-16T08:14:52Z)
• SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks [81.64530401885476]
  We propose a self-supervised LiDAR odometry method, dubbed SelfVoxeLO, to tackle these two difficulties. Specifically, we propose a 3D convolution network to process the raw LiDAR data directly, which extracts features that better encode the 3D geometric patterns. We evaluate our method's performances on two large-scale datasets, i.e., KITTI and Apollo-SouthBay.
  arXiv  Detail & Related papers  (2020-10-19T09:23:39Z)
• Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study [2.6205925938720833]
  State of the art methods use deep neural networks to predict semantic classes for each point in a LiDAR scan. A powerful and efficient way to process LiDAR measurements is to use two-dimensional, image-like projections. We demonstrate various techniques to boost the performance and to improve runtime as well as memory constraints.
  arXiv  Detail & Related papers  (2020-04-06T11:08:12Z)
• Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [82.1739023587565]
  We propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet) Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train.
  arXiv  Detail & Related papers  (2020-01-14T03:34:55Z)

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.