RoadBEV: Road Surface Reconstruction in Bird's Eye View
- URL: http://arxiv.org/abs/2404.06605v2
- Date: Sat, 20 Apr 2024 22:10:37 GMT
- Title: RoadBEV: Road Surface Reconstruction in Bird's Eye View
- Authors: Tong Zhao, Lei Yang, Yichen Xie, Mingyu Ding, Masayoshi Tomizuka, Yintao Wei,
- Abstract summary: Vision-based online road reconstruction promisingly captures road information in advance.
Recent technique of Bird's-Eye-View (BEV) perception provides immense potential to more reliable and accurate reconstruction.
This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo.
- Score: 55.0558717607946
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Road surface conditions, especially geometry profiles, enormously affect driving performance of autonomous vehicles. Vision-based online road reconstruction promisingly captures road information in advance. Existing solutions like monocular depth estimation and stereo matching suffer from modest performance. The recent technique of Bird's-Eye-View (BEV) perception provides immense potential to more reliable and accurate reconstruction. This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo, which estimate road elevation with monocular and stereo images, respectively. The former directly fits elevation values based on voxel features queried from image view, while the latter efficiently recognizes road elevation patterns based on BEV volume representing discrepancy between left and right voxel features. Insightful analyses reveal their consistence and difference with perspective view. Experiments on real-world dataset verify the models' effectiveness and superiority. Elevation errors of RoadBEV-mono and RoadBEV-stereo achieve 1.83cm and 0.50cm, respectively. The estimation performance improves by 50\% in BEV based on monocular image. Our models are promising for practical applications, providing valuable references for vision-based BEV perception in autonomous driving. The code is released at https://github.com/ztsrxh/RoadBEV.
Related papers
- Towards Efficient 3D Object Detection in Bird's-Eye-View Space for
Autonomous Driving: A Convolutional-Only Approach [13.962625803332823]
We propose an efficient BEV-based 3D detection framework called BEVENet.
BEVENet is 3$times$ faster than contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge.
Our experiments show that BEVENet is 3$times$ faster than contemporary state-of-the-art (SOTA) approaches.
arXiv Detail & Related papers (2023-12-01T14:52:59Z) - U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based
Relocalization [86.63465798307728]
Relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails.
Recent advances in Bird's-Eye-View (BEV) segmentation allow for accurate estimation of local scene appearance.
This paper presents U-BEV, a U-Net inspired architecture that extends the current state-of-the-art by allowing the BEV to reason about the scene on multiple height layers before flattening the BEV features.
arXiv Detail & Related papers (2023-10-20T18:57:38Z) - BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View [55.25826357436259]
3D Single Object Tracking (SOT) is a fundamental task of computer vision, proving essential for applications like autonomous driving.
In this paper, we propose BEVTrack, a simple yet effective baseline method.
By estimating the target motion in Bird's-Eye View (BEV) to perform tracking, BEVTrack demonstrates surprising simplicity from various aspects, i.e., network designs, training objectives, and tracking pipeline, while achieving superior performance.
arXiv Detail & Related papers (2023-09-05T12:42:26Z) - F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera
Images for Automated Driving [3.286961611175469]
We introduce a baseline, F2BEV, to generate BEV height maps and BEV semantic segmentation maps from fisheye images.
F2BEV consists of a distortion-aware spatial cross attention module for querying and consolidating spatial information.
We evaluate single-task and multi-task variants of F2BEV on our synthetic FB-SSEM dataset.
arXiv Detail & Related papers (2023-03-07T04:58:57Z) - Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception [43.080075390854205]
pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving.
This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing real-time BEV perception on the on-vehicle chips.
arXiv Detail & Related papers (2023-01-19T03:58:48Z) - Street-View Image Generation from a Bird's-Eye View Layout [95.36869800896335]
Bird's-Eye View (BEV) Perception has received increasing attention in recent years.
Data-driven simulation for autonomous driving has been a focal point of recent research.
We propose BEVGen, a conditional generative model that synthesizes realistic and spatially consistent surrounding images.
arXiv Detail & Related papers (2023-01-11T18:39:34Z) - Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view.
Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
arXiv Detail & Related papers (2022-11-15T13:52:41Z) - Delving into the Devils of Bird's-eye-view Perception: A Review,
Evaluation and Recipe [115.31507979199564]
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia.
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios.
arXiv Detail & Related papers (2022-09-12T15:29:13Z) - S-BEV: Semantic Birds-Eye View Representation for Weather and Lighting
Invariant 3-DoF Localization [5.668124846154997]
We describe a light-weight, weather and lighting invariant, Semantic Bird's Eye View (S-BEV) signature for vision-based vehicle re-localization.
arXiv Detail & Related papers (2021-01-23T19:37:09Z)
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.