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arxiv: 2411.06851 · v1 · submitted 2024-11-11 · 💻 cs.CV · cs.LG

Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction

Miguel Antunes-Garc\'ia , Luis M. Bergasa , Santiago Montiel-Mar\'in , Rafael Barea , Fabio S\'anchez-Garc\'ia , \'Angel Llamazares This is my paper

Pith reviewed 2026-05-23 17:44 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords bird's eye viewinstance predictiontransformerautonomous drivinginstance segmentationflow predictionefficient architecturemulti-camera perception
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The pith

A transformer-based BEV instance predictor uses only segmentation and flow to cut parameters and inference time versus prior multi-stage systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a new architecture for predicting object instances in bird's-eye view from multi-camera data for autonomous driving. It replaces separate detection, tracking, and prediction stages with a single simplified pipeline consisting of instance segmentation and flow prediction. An efficient transformer backbone is incorporated to lower parameter counts and speed up inference relative to existing state-of-the-art methods. Readers would care because self-driving systems require lightweight, fast models that avoid error buildup from chained processing stages and can run in real time on vehicle hardware.

Core claim

The proposed BEV instance prediction architecture achieves reduced parameter counts and inference times compared to existing SOTA architectures thanks to the incorporation of an efficient transformer-based architecture in a simplified pipeline that relies only on instance segmentation and flow prediction.

What carries the argument

Efficient transformer-based architecture that extracts and processes bird's-eye-view features for joint instance segmentation and flow prediction.

If this is right

  • Avoids error accumulation that occurs when detection, tracking, and prediction stages are handled separately.
  • Enables faster end-to-end prediction directly from multi-camera sensor data.
  • Supports real-world deployment by lowering the computational demands of BEV perception systems.
  • Gains additional speed from PyTorch 2.1 optimizations applied to the implementation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be tested for robustness when fused with LiDAR or radar inputs in low-visibility conditions.
  • Similar simplification might allow the model to handle longer prediction horizons or denser traffic scenes without raising compute costs.
  • The design pattern could transfer to other real-time perception tasks such as semantic occupancy forecasting.

Load-bearing premise

That a pipeline using only instance segmentation and flow prediction can produce accurate instance predictions without separate detection and tracking stages that would otherwise accumulate errors.

What would settle it

Standard benchmark results on autonomous driving datasets where the new model records higher instance prediction errors than current multi-stage SOTA methods would show the simplified pipeline is insufficient.

Figures

Figures reproduced from arXiv: 2411.06851 by \'Angel Llamazares, Fabio S\'anchez-Garc\'ia, Luis M. Bergasa, Miguel Antunes-Garc\'ia, Rafael Barea, Santiago Montiel-Mar\'in.

Figure 1
Figure 1. Figure 1: Our proposed architecture uses a multi-camera system to predict [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Diagram of the proposed architecture. First, the features of all the images are extracted for the whole input sequence. Each set of features of each instant is projected to the BEV using the information generated in the depth channels. For the past frames, it is necessary to apply a transformation that translates them to a unified system in the present frame. The generated BEV feature map is applied to two… view at source ↗
Figure 3
Figure 3. Figure 3: Head architecture for the segmentation and flow branches. C. Flow warping To obtain the desired final representation of the different instances, it is necessary to propagate the information along the sequence. As described in [6] and shown in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results on NuScenes validation set. Each detected instance is represented by a color. If future positions are predicted, they are represented with the same color but with transparency. The ego vehicle is represented in black at the center. an optimizer, we used AdamW, which was initialized with a Learning Rate (LR) of 6e − 5 and gradually decreased according to a Polynomial scheduler. First of … view at source ↗
read the original abstract

Accurate object detection and prediction are critical to ensure the safety and efficiency of self-driving architectures. Predicting object trajectories and occupancy enables autonomous vehicles to anticipate movements and make decisions with future information, increasing their adaptability and reducing the risk of accidents. Current State-Of-The-Art (SOTA) approaches often isolate the detection, tracking, and prediction stages, which can lead to significant prediction errors due to accumulated inaccuracies between stages. Recent advances have improved the feature representation of multi-camera perception systems through Bird's-Eye View (BEV) transformations, boosting the development of end-to-end systems capable of predicting environmental elements directly from vehicle sensor data. These systems, however, often suffer from high processing times and number of parameters, creating challenges for real-world deployment. To address these issues, this paper introduces a novel BEV instance prediction architecture based on a simplified paradigm that relies only on instance segmentation and flow prediction. The proposed system prioritizes speed, aiming at reduced parameter counts and inference times compared to existing SOTA architectures, thanks to the incorporation of an efficient transformer-based architecture. Furthermore, the implementation of the proposed architecture is optimized for performance improvements in PyTorch version 2.1. Code and trained models are available at https://github.com/miguelag99/Efficient-Instance-Prediction

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes a novel BEV instance prediction architecture that simplifies the conventional detection-tracking-prediction cascade into a single pipeline relying only on instance segmentation and flow prediction. It incorporates an efficient transformer-based design, optimized in PyTorch 2.1, with the central claim being reduced parameter counts and inference times relative to existing SOTA methods; code and trained models are released publicly.

Significance. If the efficiency claims hold while maintaining competitive accuracy, the work could support real-time deployment of BEV perception in autonomous vehicles by mitigating error accumulation across stages. The public release of code and models is a clear strength for reproducibility. However, the absence of any supporting quantitative evidence in the manuscript leaves the practical impact difficult to assess.

major comments (2)
  1. [Abstract] Abstract: The central claims of 'reduced parameter counts and inference times compared to existing SOTA architectures' are asserted without any quantitative results, baseline comparisons, error metrics (e.g., instance mAP, minFDE, occupancy IoU), or ablation studies, leaving the primary contribution without visible empirical support.
  2. [Abstract] The manuscript's premise that the simplified segmentation+flow pipeline suffices for accurate instance-level predictions (instance association, trajectory consistency) without separate detection/tracking stages is load-bearing for the efficiency argument, yet no accuracy metrics or head-to-head comparisons are supplied to test whether accuracy is preserved or degraded.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments correctly note that the manuscript's efficiency claims and the validity of the simplified pipeline require stronger quantitative backing. We address each point below and will revise the manuscript to incorporate the requested evidence.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of 'reduced parameter counts and inference times compared to existing SOTA architectures' are asserted without any quantitative results, baseline comparisons, error metrics (e.g., instance mAP, minFDE, occupancy IoU), or ablation studies, leaving the primary contribution without visible empirical support.

    Authors: We agree that the abstract and manuscript would be strengthened by including explicit quantitative support. We will revise the abstract to report specific reductions in parameter counts and inference times versus SOTA. The revised manuscript will also feature the baseline comparisons, error metrics (instance mAP, minFDE, occupancy IoU), and ablation studies to provide full empirical grounding for the contribution. revision: yes

  2. Referee: [Abstract] The manuscript's premise that the simplified segmentation+flow pipeline suffices for accurate instance-level predictions (instance association, trajectory consistency) without separate detection/tracking stages is load-bearing for the efficiency argument, yet no accuracy metrics or head-to-head comparisons are supplied to test whether accuracy is preserved or degraded.

    Authors: This observation is accurate and directly relevant to the core premise. We will add accuracy metrics and head-to-head comparisons against multi-stage SOTA methods in the revision. These additions will allow evaluation of whether instance association and trajectory consistency are preserved under the simplified segmentation+flow approach. revision: yes

Circularity Check

0 steps flagged

No circularity detected in architectural proposal

full rationale

The paper presents a new BEV instance prediction architecture based on instance segmentation plus flow prediction and an efficient transformer design. No equations, fitted parameters, or self-citations are shown that reduce the claimed efficiency gains or accuracy to quantities defined by the authors' own inputs. The derivation chain consists of a standard architectural proposal with external SOTA comparisons and is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on the abstract alone, the central claim rests on the design choice of a simplified two-task paradigm and standard transformer components; no explicit free parameters, new entities, or ad-hoc axioms are identifiable.

axioms (1)
  • domain assumption Instance segmentation combined with flow prediction is sufficient to replace multi-stage detection-tracking-prediction pipelines for accurate BEV instance prediction
    The architecture description in the abstract explicitly relies only on these two components.

pith-pipeline@v0.9.0 · 5791 in / 1366 out tokens · 46331 ms · 2026-05-23T17:44:08.259869+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    Learning lane graph representations for motion forecasting,

    M. Liang, B. Yang, R. Hu, Y . Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” 2020

    work page 2020

  2. [2]

    Crat-pred: Vehicle trajectory prediction with crystal graph convolutional neural networks and multi-head self-attention,

    J. Schmidt, J. Jordan, F. Gritschneder, and K. Dietmayer, “Crat-pred: Vehicle trajectory prediction with crystal graph convolutional neural networks and multi-head self-attention,” 2022

    work page 2022

  3. [3]

    Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,

    J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in ECCV 2020, A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds., 2020, pp. 194–210

    work page 2020

  4. [4]

    Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,

    A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V . Badrinarayanan, R. Cipolla, and A. Kendall, “Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 15 273–15 282

    work page 2021

  5. [5]

    Beverse: Unified perception and prediction in birds-eye-view for vision-centric autonomous driving,

    Y . Zhang, Z. Zhu, W. Zheng, J. Huang, G. Huang, J. Zhou, and J. Lu, “Beverse: Unified perception and prediction in birds-eye-view for vision-centric autonomous driving,” 2022

    work page 2022

  6. [6]

    Powerbev: A powerful yet lightweight framework for instance pre- diction in bird’s-eye view,

    P. Li, S. Ding, X. Chen, N. Hanselmann, M. Cordts, and J. Gall, “Powerbev: A powerful yet lightweight framework for instance pre- diction in bird’s-eye view,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23 , 8 2023, pp. 1080–1088

    work page 2023

  7. [7]

    Are we ready for autonomous driving? the kitti vision benchmark suite,

    A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012

    work page 2012

  8. [8]

    Fcos3d: Fully convolutional one-stage monocular 3d object detection,

    T. Wang, X. Zhu, J. Pang, and D. Lin, “Fcos3d: Fully convolutional one-stage monocular 3d object detection,” in 2021 IEEE/CVF Inter- national Conference on Computer Vision Workshops (ICCVW) , 2021, pp. 913–922

    work page 2021

  9. [9]

    Probabilistic and geometric depth: Detecting objects in perspective,

    T. Wang, X. Zhu, J. Pang, and D. Lin, “Probabilistic and geometric depth: Detecting objects in perspective,” 2021

    work page 2021

  10. [10]

    Smoke: Single-stage monocular 3d object detection via keypoint estimation,

    Z. Liu, Z. Wu, and R. T ´oth, “Smoke: Single-stage monocular 3d object detection via keypoint estimation,” 2020

    work page 2020

  11. [11]

    Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving,

    Y . You, Y . Wang, W.-L. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving,” 2020

    work page 2020

  12. [12]

    nuscenes: A multimodal dataset for autonomous driving,

    H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR, 2020

    work page 2020

  13. [13]

    Detr3d: 3d object detection from multi-view images via 3d-to-2d queries,

    Y . Wang, V . C. Guizilini, T. Zhang, Y . Wang, H. Zhao, and J. Solomon, “Detr3d: 3d object detection from multi-view images via 3d-to-2d queries,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 2022, pp. 180–191

    work page 2022

  14. [14]

    Bev- former: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,

    Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Q. Yu, and J. Dai, “Bev- former: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” 2022

    work page 2022

  15. [15]

    Multi-head attention for multi-modal joint vehicle motion forecasting,

    J. Mercat, T. Gilles, N. El Zoghby, G. Sandou, D. Beauvois, and G. P. Gil, “Multi-head attention for multi-modal joint vehicle motion forecasting,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 9638–9644

    work page 2020

  16. [16]

    Attention based vehicle trajectory prediction,

    K. Messaoud, I. Yahiaoui, A. Verroust-Blondet, and F. Nashashibi, “Attention based vehicle trajectory prediction,” IEEE Transactions on Intelligent Vehicles, vol. 6, no. 1, pp. 175–185, 2021

    work page 2021

  17. [17]

    Exploring attention gan for vehicle motion prediction,

    C. G ´omez-Hu´elamo, M. V . Conde, M. Ortiz, S. Montiel, R. Barea, and L. M. Bergasa, “Exploring attention gan for vehicle motion prediction,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) , 2022, pp. 4011–4016

    work page 2022

  18. [18]

    Fast and furious: Real time end- to-end 3d detection, tracking and motion forecasting with a single convolutional net,

    W. Luo, B. Yang, and R. Urtasun, “Fast and furious: Real time end- to-end 3d detection, tracking and motion forecasting with a single convolutional net,” in2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 3569–3577

    work page 2018

  19. [19]

    Tbp- former: Learning temporal bird’s-eye-view pyramid for joint per- ception and prediction in vision-centric autonomous driving,

    S. Fang, Z. Wang, Y . Zhong, J. Ge, S. Chen, and Y . Wang, “Tbp- former: Learning temporal bird’s-eye-view pyramid for joint per- ception and prediction in vision-centric autonomous driving,” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1368–1378, 2023

    work page 2023

  20. [20]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , I. Guyon, U. V . Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30, 2017

    work page 2017

  21. [21]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” 2021

    work page 2021

  22. [22]

    Swin transformer: Hierarchical vision transformer using shifted windows,

    Z. Liu, Y . Lin, Y . Cao, H. Hu, Y . Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” 2021

    work page 2021

  23. [23]

    Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,

    W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” 2021

    work page 2021

  24. [24]

    Segformer: Simple and efficient design for semantic segmentation with transformers,

    E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” in Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y . Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. 34, 2021, pp. 12 077–12 090

    work page 2021

  25. [25]

    Efficientnet: Rethinking model scaling for convolutional neural networks,

    M. Tan and Q. V . Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” 2020

    work page 2020

  26. [26]

    Akan and F

    A. Akan and F. Guney, StretchBEV: Stretching Future Instance Pre- diction Spatially and Temporally , 10 2022, pp. 444–460

    work page 2022