FlashBEV: Fast and Memory-Efficient Exact BEV Transformation with IO-Awareness

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 uni(cid:28)ed 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(cid:22)which we refer to as Tensorized Sampling-VT(cid:22)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(cid:21)reduction pattern: each BEV query independently accumulates contributions across cameras and height bins. Unlike splatting-based VT, which requires index sorting and prevents fully thread-local reduction from voxel construction to BEV output, the gather(cid:21)reduction structure of Sampling-VT enables thread-local accumulation with on-the-(cid:29)y recomputation, eliminating the need to materialize heightand camera-dependent intermediates. Based on this insight, we propose FlashBEV, a fully fused and IOaware execution strategy that is mathematically equivalent to Tensorized Sampling-VT (same operator output) while substantially reducing global memory tra(cid:30)c and kernel-launch overhead. Experiments show that FlashBEV achieves more than an order of magnitude lower peak GPU memory and signi(cid:28)cant inference-latency speedups, with memory usage 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 (cid:28)xed deployment budgets on memory-constrained devices, where tensorized execution would otherwise be infeasible. Our contribution is therefore an execution redesign(cid:22)same math, di(cid:27)erent execution(cid:22)that removes a key scalability barrier for deployment-ready Sampling-VT. Code is available at https://github.com/yokosyun/FlashBEV. • •