On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection
Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been shown to enhance the performance of cyber-physical systems. In this work, we focus on enabling anytime computing for deep neural networks (DNNs) that process LiDAR point clouds for 3D object detection. We propose a novel method that enables multi-resolution inference for models that process point clouds as pillars or voxels, allowing the input to be dynamically scaled and processed at the resolution needed to meet timing requirements. Importantly, our memory-efficient approach requires the deployment of only a single DNN model, avoiding the need...
| Authors | Ahmet Soyyigit, Shuochao Yao, Heechul Yun |
|---|---|
| Primary category | cs.RO |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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