Flexible 3D Lane Detection by Hierarchical Shape MatchingFlexible 3D Lane Detection by Hierarchical Shape Matching

• URL: http://arxiv.org/abs/2408.07163v1
• Date: Tue, 13 Aug 2024 19:04:23 GMT
• Title: Flexible 3D Lane Detection by Hierarchical Shape MatchingFlexible 3D Lane Detection by Hierarchical Shape Matching
• Authors: Zhihao Guan, Ruixin Liu, Zejian Yuan, Ao Liu, Kun Tang, Tong Zhou, Erlong Li, Chao Zheng, Shuqi Mei,
• Abstract summary: 3D lane detection is still an open problem due to varying visual conditions, complex typologies, and strict demands for precision. In this paper, an end-to-end flexible and hierarchical lane detector is proposed to precisely predict 3D lane lines from point clouds.
• Score: 29.038755629481035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As one of the basic while vital technologies for HD map construction, 3D lane detection is still an open problem due to varying visual conditions, complex typologies, and strict demands for precision. In this paper, an end-to-end flexible and hierarchical lane detector is proposed to precisely predict 3D lane lines from point clouds. Specifically, we design a hierarchical network predicting flexible representations of lane shapes at different levels, simultaneously collecting global instance semantics and avoiding local errors. In the global scope, we propose to regress parametric curves w.r.t adaptive axes that help to make more robust predictions towards complex scenes, while in the local vision the structure of lane segment is detected in each of the dynamic anchor cells sampled along the global predicted curves. Moreover, corresponding global and local shape matching losses and anchor cell generation strategies are designed. Experiments on two datasets show that we overwhelm current top methods under high precision standards, and full ablation studies also verify each part of our method. Our codes will be released at https://github.com/Doo-do/FHLD.

Related papers

• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• Double-Shot 3D Shape Measurement with a Dual-Branch Network [14.749887303860717]
  We propose a dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet) to process different structured light (SL) modalities. Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images. We show that our method can reduce fringe order ambiguity while producing high-accuracy results on a self-made dataset.
  arXiv  Detail & Related papers  (2024-07-19T10:49:26Z)
• CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention [6.337799395191661]
  We present CurveFormer++, a single-stage Transformer-based method that does not require the image feature view transform module. By employing a Transformer decoder, the model can iteratively refine the 3D lane detection results. We evaluate our approach for the 3D lane detection task on two publicly available real-world datasets.
  arXiv  Detail & Related papers  (2024-02-09T14:13:40Z)
• 3D Lane Detection from Front or Surround-View using Joint-Modeling & Matching [27.588395086563978]
  We propose a joint modeling approach that combines Bezier curves and methods. We also introduce a novel 3D Spatial, representing an exploration of 3D surround-view lane detection research. This innovative method establishes a new benchmark in front-view 3D lane detection on the Openlane dataset.
  arXiv  Detail & Related papers  (2024-01-16T01:12:24Z)
• Deformation-Guided Unsupervised Non-Rigid Shape Matching [7.327850781641328]
  We present an unsupervised data-driven approach for non-rigid shape matching. Our approach is particularly robust when matching digitized shapes using 3D scanners.
  arXiv  Detail & Related papers  (2023-11-27T09:55:55Z)
• Decoupling the Curve Modeling and Pavement Regression for Lane Detection [67.22629246312283]
  curve-based lane representation is a popular approach in many lane detection methods. We propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression.
  arXiv  Detail & Related papers  (2023-09-19T11:24:14Z)
• Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation [53.19926259132379]
  We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. An attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals.
  arXiv  Detail & Related papers  (2023-05-10T03:40:25Z)
• Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation [53.04781510348416]
  Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. We propose to structurally decouple the modeling of long-term and short-term correlations in an end-to-end framework, Global-to-Local Transformer (GLoT) Our GLoT surpasses previous state-of-the-art methods with the lowest model parameters on popular benchmarks, i.e., 3DPW, MPI-INF-3DHP, and Human3.6M.
  arXiv  Detail & Related papers  (2023-03-26T14:57:49Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport [80.64516377977183]
  Shape matching has been a long-studied problem for the computer graphics and vision community. We investigate a hierarchical learning design, to which we incorporate local patch-level information and global shape-level structures. We propose a novel optimal transport solver by recurrently updating features on non-confident nodes to learn globally consistent correspondences between the shapes.
  arXiv  Detail & Related papers  (2022-02-03T11:41:46Z)
• Focus on Local: Detecting Lane Marker from Bottom Up via Key Point [10.617793053931964]
  We propose a novel lane marker detection solution, FOLOLane, that focuses on modeling local patterns and achieving prediction of global structures. Specifically, the CNN models lowcomplexity local patterns with two separate heads, the first one predicts the existence of key points, and the second refines the location of key points in the local range and correlates key points of the same lane line.
  arXiv  Detail & Related papers  (2021-05-28T08:59:14Z)

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.