Cross-view Transformers for real-time Map-view Semantic Segmentation
Not on the map. Why?
Cited by 22 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present cross-view transformers, an efficient attention-based model for map-view semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism. Each camera uses positional embeddings that depend on its intrinsic and extrinsic calibration. These embeddings allow a transformer to learn the mapping across different views without ever explicitly modeling it geometrically. The architecture consists of a convolutional image encoder for each view and cross-view transformer layers to infer a map-view semantic segmentation. Our model is simple, easily parallelizable, and runs in real-time. The presented architecture performs at state-of-the-art on the nuScenes dataset, with 4x faster inference speeds. Code is available at https://github.com/bradyz/cross_view_transformers.
Key facts. Cited by 264 papers in the map (data through 7 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 5 May 2022.
Builds on
32 of 49 references.
References · 32
- 2017Attention Is All You Needcited by 59,932
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
- 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
- 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
- 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
- 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
- 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
- 2019Objects as Pointscited by 1,172
- 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,040
- 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
20 more
- 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
- 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
- 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
- 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 455
- 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
- 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 239
- 2020Atlas: End-to-End 3D Scene Reconstruction from Posed Imagescited by 206
- 2018Orthographic Feature Transform for Monocular 3D Object Detectioncited by 201
- 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Ratescited by 196
- 2020Predicting Semantic Map Representations from Images using Pyramid Occupancy Networkscited by 175
- 2019Cross-view Semantic Segmentation for Sensing Surroundingscited by 173
- 2019Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Drivingcited by 137
- 2018ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shapecited by 123
- 2019Monocular 3D Object Detection with Pseudo-LiDAR Point Cloudcited by 113
- 20183D-LaneNet: End-to-End 3D Multiple Lane Detectioncited by 106
- 2018Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networkscited by 84
- 2020MonoLayout: Amodal scene layout from a single imagecited by 49
- 2019A Geometric Approach to Obtain a Bird's Eye View from an Imagecited by 34
- 2020Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planningcited by 24
- 2021Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homographycited by 21
Built on by
Citing papers per year: 2022: 27; 2023: 82; 2024: 91; 2025: 41; 2026: 23.
264 papers.
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