用更少内存和更快速度实现高精度的摄像头视角转换。
FlashBEV: Fast and Memory-Efficient Exact BEV Transformation with IO-Awareness
- 通过重写执行流程，避免生成大中间张量，降低内存占用。
- 峰值显存降至原来的十分之一，推理延迟显著降低。
- 适合在内存受限设备上部署高分辨率、长距离感知系统。
鸟瞰图（BEV）感知是自动驾驶中基于摄像头的3D理解核心组件，视图变换（VT）将多摄像头图像特征映射到统一的BEV表示。基于采样的视图变换（Sampling-VT）因支持密集连续的BEV聚合而受到关注，但其部署瓶颈在于系统层面：标准的张量化实现——我们称为张量化Sampling-VT——会显式生成依赖高度的大规模中间张量，导致内存与延迟随垂直分辨率和摄像头数量增长而急剧上升。本文从算子执行角度重新审视该方法，发现其遵循收集-归约模式：每个BEV查询可独立累积来自各摄像头和高度区间的贡献，支持线程内局部累加与即时重计算，从而无需存储依赖高度和摄像头的中间结果。基于此，我们提出FlashBEV，一种完全融合且输入输出感知的执行策略，数学上等价于张量化Sampling-VT（输出一致），但大幅减少全局内存访问与内核启动开销。实验表明，FlashBEV实现峰值GPU内存降低一个数量级以上，推理延迟显著提升，内存消耗基本与高度分块数无关，峰值内存仅为O(BCXY)（仅输出）。这使得在固定内存预算下可实现更高范围/分辨率及更细粒度的垂直离散化。本工作通过执行重构（相同数学，不同执行）消除了部署级Sampling-VT的关键可扩展性障碍。代码已开源。
原文摘要 · Abstract (English)
Bird's-eye-view (BEV) perception is a core component of camera-based 3D understanding in autonomous driving, where view transformation (VT) maps multi-camera image features into a unified BEV representation. Sampling-based view transformation (Sampling-VT) is attractive because it supports dense and continuous BEV aggregation for high-resolution and long-range perception. Its deployment bottleneck, however, is systems-level: standard tensorized implementations of Sampling-VT -- which we refer to as Tensorized Sampling-VT -- explicitly materialize large height-dependent intermediate tensors, causing memory and latency costs that scale poorly with vertical resolution and the number of cameras. We revisit Tensorized Sampling-VT from an operator-execution perspective and show that it follows a gather-reduction pattern: each BEV query independently accumulates contributions across cameras and height bins, enabling thread-local accumulation with on-the-fly recomputation that eliminates the need to materialize height- and camera-dependent intermediates. Based on this insight, we propose FlashBEV, a fully fused and IO-aware execution strategy mathematically equivalent to Tensorized Sampling-VT (same operator output) while substantially reducing global memory traffic and kernel-launch overhead. Experiments show that FlashBEV achieves more than an order of magnitude lower peak GPU memory and significant inference-latency speedups, with memory effectively independent of the number of height bins, reducing the operator's peak memory to O(BCXY) (output only). This unlocks higher BEV range/resolution and vertical discretization within fixed deployment budgets on memory-constrained devices. Our contribution is an execution redesign -- same math, different execution -- that removes a key scalability barrier for deployment-ready Sampling-VT. Code available at https://github.com/yokosyun/FlashBEV
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