Adaptive-Cloud: Dynamic Computation Control for 3D Object Detection from LIDAR Point Clouds

Abstract

In this work, we introduce an adaptive hierarchical framework for efficient 3D object detection from point cloud data, designed to dynamically balance computational efficiency and detection performance. Our approach employs a shared feature extractor and multiple detector backbones of varying widths, enabling selective activation of models based on the complexity of the input scene. A novel feature gating mechanism dynamically determines the most relevant features for reduced-width backbones, while a surrogate loss prediction module ranks models in real-time, ensuring optimal backbone selection with minimal overhead. This adaptive strategy reduces computational costs by 41.4% while maintaining a negligible 2.44% reduction in detection accuracy across a range of real-world driving scenes (urban, highway, residential, campus, person) from the KITTI dataset. By addressing runtime adaptability—a critical gap in existing 3D detection frameworks—our method provides a significant improvement for deploying high-performance detection models in resource-constrained environments.