Focus on BEV: Self-calibrated Cycle View Transformation for Monocular Birds-Eye-View Segmentation

• URL: http://arxiv.org/abs/2410.15932v1
• Date: Mon, 21 Oct 2024 12:00:52 GMT
• Title: Focus on BEV: Self-calibrated Cycle View Transformation for Monocular Birds-Eye-View Segmentation
• Authors: Jiawei Zhao, Qixing Jiang, Xuede Li, Junfeng Luo,
• Abstract summary: Birds--EyeView (BEV) segmentation aims to establish a spatial mapping from the perspective view to the top view. Recent studies have encountered difficulties in view transformation due to the disruption of BEV-agnostic features in image space.
• Score: 4.9185678564997355
• License:
• Abstract: Birds-Eye-View (BEV) segmentation aims to establish a spatial mapping from the perspective view to the top view and estimate the semantic maps from monocular images. Recent studies have encountered difficulties in view transformation due to the disruption of BEV-agnostic features in image space. To tackle this issue, we propose a novel FocusBEV framework consisting of $(i)$ a self-calibrated cross view transformation module to suppress the BEV-agnostic image areas and focus on the BEV-relevant areas in the view transformation stage, $(ii)$ a plug-and-play ego-motion-based temporal fusion module to exploit the spatiotemporal structure consistency in BEV space with a memory bank, and $(iii)$ an occupancy-agnostic IoU loss to mitigate both semantic and positional uncertainties. Experimental evidence demonstrates that our approach achieves new state-of-the-art on two popular benchmarks,\ie, 29.2\% mIoU on nuScenes and 35.2\% mIoU on Argoverse.

Related papers

• VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization [108.68014173017583]
  Bird's-eye-view (BEV) map layout estimation requires an accurate and full understanding of the semantics for the environmental elements around the ego car. We propose to utilize a generative model similar to the Vector Quantized-Variational AutoEncoder (VQ-VAE) to acquire prior knowledge for the high-level BEV semantics in the tokenized discrete space. Thanks to the obtained BEV tokens accompanied with a codebook embedding encapsulating the semantics for different BEV elements in the groundtruth maps, we are able to directly align the sparse backbone image features with the obtained BEV tokens
  arXiv  Detail & Related papers  (2024-11-03T16:09:47Z)
• DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception [104.87876441265593]
  Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. Unsupervised domain adaptive BEV, which effective learning from various unlabelled target data, is far under-explored. We design DA-BEV, the first domain adaptive camera-only BEV framework that addresses domain adaptive BEV challenges by exploiting the complementary nature of image-view features and BEV features.
  arXiv  Detail & Related papers  (2024-01-13T04:21:24Z)
• Bird's-Eye-View Scene Graph for Vision-Language Navigation [85.72725920024578]
  Vision-language navigation (VLN) entails an agent to navigate 3D environments following human instructions. We present a BEV Scene Graph (BSG), which leverages multi-step BEV representations to encode scene layouts and geometric cues of indoor environment. Based on BSG, the agent predicts a local BEV grid-level decision score and a global graph-level decision score, combined with a sub-view selection score on panoramic views.
  arXiv  Detail & Related papers  (2023-08-09T07:48:20Z)
• FB-BEV: BEV Representation from Forward-Backward View Transformations [131.11787050205697]
  We propose a novel View Transformation Module (VTM) for Bird-Eye-View (BEV) representation. We instantiate the proposed module with FB-BEV, which achieves a new state-of-the-art result of 62.4% NDS on the nuScenes test set.
  arXiv  Detail & Related papers  (2023-08-04T10:26:55Z)
• Leveraging BEV Representation for 360-degree Visual Place Recognition [14.497501941931759]
  This paper investigates the advantages of using Bird's Eye View representation in 360-degree visual place recognition (VPR) We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets.
  arXiv  Detail & Related papers  (2023-05-23T08:29:42Z)
• BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision [101.36648828734646]
  We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2022-11-18T18:59:48Z)
• GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation [105.19949897812494]
  Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving. We present a novel two-stage Geometry Prior-based Transformation framework named GitNet.
  arXiv  Detail & Related papers  (2022-04-16T06:46:45Z)
• Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images [4.449481309681663]
  We present the first end-to-end learning approach for directly predicting dense panoptic segmentation maps in the Bird's-Eye-View (BEV) maps. Our architecture follows the top-down paradigm and incorporates a novel dense transformer module. We derive a mathematical formulation for the sensitivity of the FV-BEV transformation which allows us to intelligently weight pixels in the BEV space.
  arXiv  Detail & Related papers  (2021-08-06T17:59:11Z)

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.