  Bird's Eye View (BEV) is a popular representation for processing 3D point
clouds, and by its nature is fundamentally sparse. Motivated by the
computational limitations of mobile robot platforms, we create a fast,
high-performance BEV 3D object detector that maintains and exploits this input
sparsity to decrease runtimes over non-sparse baselines and avoids the tradeoff
between pseudoimage area and runtime. We present results on KITTI, a canonical
3D detection dataset, and Matterport-Chair, a novel Matterport3D-derived chair
detection dataset from scenes in real furnished homes. We evaluate runtime
characteristics using a desktop GPU, an embedded ML accelerator, and a robot
CPU, demonstrating that our method results in significant detection speedups
(2X or more) for embedded systems with only a modest decrease in detection
quality. Our work represents a new approach for practitioners to optimize
models for embedded systems by maintaining and exploiting input sparsity
throughout their entire pipeline to reduce runtime and resource usage while
preserving detection performance.
