PointBeV: A Sparse Approach to BeV Predictions (2312.00703v2)

Abstract: Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications, offering a unified space for sensor data fusion and supporting various downstream tasks. However, conventional models use grids with fixed resolution and range and face computational inefficiencies due to the uniform allocation of resources across all cells. To address this, we propose PointBeV, a novel sparse BeV segmentation model operating on sparse BeV cells instead of dense grids. This approach offers precise control over memory usage, enabling the use of long temporal contexts and accommodating memory-constrained platforms. PointBeV employs an efficient two-pass strategy for training, enabling focused computation on regions of interest. At inference time, it can be used with various memory/performance trade-offs and flexibly adjusts to new specific use cases. PointBeV achieves state-of-the-art results on the nuScenes dataset for vehicle, pedestrian, and lane segmentation, showcasing superior performance in static and temporal settings despite being trained solely with sparse signals. We will release our code along with two new efficient modules used in the architecture: Sparse Feature Pulling, designed for the effective extraction of features from images to BeV, and Submanifold Attention, which enables efficient temporal modeling. Our code is available at https://github.com/valeoai/PointBeV.

• The paper introduces PointBeV, a sparse segmentation model that directs computational resources to regions of interest in Bird's-Eye View predictions.
• It employs innovative Sparse Feature Modules and a two-pass training strategy to efficiently handle temporal context and reduce computational demands.
• The method achieves state-of-the-art IoU performance on vehicle, pedestrian, and lane segmentation tasks, making it ideal for edge-computing in autonomous systems.

PointBeV: An Efficient Sparse Bird's-Eye View Segmentation Model

The paper "PointBeV: A Sparse Approach to BeV Predictions" addresses the challenges of computational inefficiency and resource allocation in Bird's-Eye View (BeV) representations commonly used in autonomous driving applications. Traditional approaches use dense grids which lead to uniform computational demand across all cells, making them less efficient. PointBeV, the method proposed in this paper, adopts a sparse approach for BeV segmentation, optimizing resources and offering state-of-the-art performance on various segmentation tasks with minimal computational overhead.

Key Contributions

This paper introduces PointBeV, which operates on sparse BeV cells rather than dense grids. This sparse segmentation model is optimized for memory efficiency and allows for the use of extensive temporal context, making it suitable for edge-computing environments. By focusing computational resources on regions of interest, PointBeV can balance performance and efficiency according to specific application requirements without compromising state-of-the-art results.

Notable contributions include:

1. Sparse Feature Modules: The introduction of Sparse Feature Pulling and Submanifold Attention modules facilitates efficient feature extraction and temporal modeling. These modules are crucial in achieving the computational efficiency observed in PointBeV, as they enable the system to handle sparse inputs effectively.
2. Two-Pass Training Strategy: The strategy involves an initial 'coarse' pass that sparsely samples BeV points followed by a 'fine' pass focusing on regions marked as important in the first pass. This results in training stability and efficiency, significantly reducing the number of BeV points required.
3. Adaptability of Inference: PointBeV can adapt at inference time, employing diverse strategies for different use-cases. It allows selective computations based on environmental inputs, making it flexible for practical deployments.

Evaluation and Results

The model achieves leading performance on the nuScenes dataset for tasks such as vehicle, pedestrian, and lane segmentation in both static and dynamic setups. Significant numerical results substantiate its performance:

• Vehicle Segmentation: It surpasses competitive baselines like Simple-BEV and BEVFormer, achieving higher Intersection over Union (IoU) scores with varying visibility filters and resolutions.
• Pedestrian and Lane Segmentation: PointBeV sets new benchmarks, significantly outperforming previous state-of-the-art methods.

The approach also shows that with sparse sampling, achieving comparable performance to dense sampling requires substantially fewer computational resources, highlighting its efficiency.

Implications and Future Directions

PointBeV's sparse approach introduces a paradigm shift in BeV-based perception models, showcasing how resource allocation can be optimized without sacrificing performance. This has profound implications for edge computing in autonomous vehicles, where computational resources are constrained.

Future AI advancements could build on PointBeV by exploring its adaptability in other domains requiring efficient resource allocation. The sparse inference strategy might be extended to handle additional real-world complexities or combined with further unsupervised learning methods to improve model generalization.

This paper demonstrates significant progress in resource-efficient computing for complex perception tasks while setting groundwork for future enhancements in the field of BeV segmentations through sparse computation methodologies.