Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach

• URL: http://arxiv.org/abs/2312.00633v2
• Date: Tue, 08 Oct 2024 09:48:41 GMT
• Title: Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach
• Authors: Yuxin Li, Qiang Han, Mengying Yu, Yuxin Jiang, Chaikiat Yeo, Yiheng Li, Zihang Huang, Nini Liu, Hsuanhan Chen, Xiaojun Wu,
• Abstract summary: 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.
• Score: 13.513005108086006
• License:
• Abstract: 3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving. Despite the demonstrated improvements in accuracy and velocity estimation compared to perspective view methods, the deployment of BEV-based techniques in real-world autonomous vehicles remains challenging. This is primarily due to their reliance on vision-transformer (ViT) based architectures, which introduce quadratic complexity with respect to the input resolution. To address this issue, we propose an efficient BEV-based 3D detection framework called BEVENet, which leverages a convolutional-only architectural design to circumvent the limitations of ViT models while maintaining the effectiveness of BEV-based methods. Our experiments show that BEVENet is 3$\times$ faster than contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge, achieving a mean average precision (mAP) of 0.456 and a nuScenes detection score (NDS) of 0.555 on the NuScenes validation dataset, with an inference speed of 47.6 frames per second. To the best of our knowledge, this study stands as the first to achieve such significant efficiency improvements for BEV-based methods, highlighting their enhanced feasibility for real-world autonomous driving applications.

Related papers

• Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving [55.93813178692077]
  We present RoboBEV, an extensive benchmark suite designed to evaluate the resilience of BEV algorithms. We assess 33 state-of-the-art BEV-based perception models spanning tasks like detection, map segmentation, depth estimation, and occupancy prediction. Our experimental results also underline the efficacy of strategies like pre-training and depth-free BEV transformations in enhancing robustness against out-of-distribution data.
  arXiv  Detail & Related papers  (2024-05-27T17:59:39Z)
• Diffusion-Based Particle-DETR for BEV Perception [94.88305708174796]
  Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV.
  arXiv  Detail & Related papers  (2023-12-18T09:52:14Z)
• Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning [93.71280187657831]
  Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. We propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector.
  arXiv  Detail & Related papers  (2023-12-13T09:24:42Z)
• OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection [29.530177591608297]
  Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query-based bird's-eye-view (BEV) paradigm. We propose an Object-Centric query-BEV detector OCBEV, which can carve the temporal and spatial cues of moving targets more effectively.
  arXiv  Detail & Related papers  (2023-06-02T17:59:48Z)
• CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception [32.91233926771015]
  CALICO is a novel framework that applies contrastive objectives to both LiDAR and camera backbones. Our framework can be tailored to different backbones and heads, positioning it as a promising approach for multimodal BEV perception.
  arXiv  Detail & Related papers  (2023-06-01T05:06:56Z)
• Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving [31.98600806479808]
  Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. We evaluate the natural and adversarial robustness of various representative models under extensive settings. We propose a 3D consistent patch attack by applying adversarial patches in thetemporal 3D space to guarantee the consistency.
  arXiv  Detail & Related papers  (2023-03-30T11:16:58Z)
• Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline [76.48192454417138]
  Bird's-Eye View (BEV) representation is promising as the foundation for next-generation Autonomous Vehicle (AV) perception. This paper proposes a framework, termed Fast-BEV, which is capable of performing faster BEV perception on the on-vehicle chips.
  arXiv  Detail & Related papers  (2023-01-29T18:43:31Z)
• 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)
• BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios [51.285561119993105]
  We present BEV-MAE, an efficient masked autoencoder pre-training framework for LiDAR-based 3D object detection in autonomous driving. Specifically, we propose a bird's eye view (BEV) guided masking strategy to guide the 3D encoder learning feature representation. We introduce a learnable point token to maintain a consistent receptive field size of the 3D encoder.
  arXiv  Detail & Related papers  (2022-12-12T08:15:03Z)
• PersDet: Monocular 3D Detection in Perspective Bird's-Eye-View [26.264139933212892]
  Bird's-Eye-View (BEV) is superior to other 3D detectors for autonomous driving and robotics. transforming image features into BEV necessitates special operators to conduct feature sampling. We propose detecting objects in perspective BEV -- a new BEV representation that does not require feature sampling.
  arXiv  Detail & Related papers  (2022-08-19T15:19:20Z)
• Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
  We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
  arXiv  Detail & Related papers  (2021-04-22T12:47:37Z)

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.