VoxAdapt: Adaptive Multi-Scale 3D Object Detection for Real-Time Mobile Applications and Edge Systems - Pathiraja, Daham; Perera, Indika (International Journal of Interactive Mobile Technologies (iJIM), 2026)
Deploying real-time 3D perception capabilities on mobile and edge platforms, such as autonomous robots, drones, and intelligent sensing systems, requires balancing detection accuracy against strict computation and memory constraints. Existing voxel-based LiDAR perception pipelines rely on fixed voxel sizes that must be manually tuned for each dataset and sensor, limiting their adaptability across deployment environments. We introduce VoxAdapt, an adaptive multi-scale 3D object detection framework that treats voxel-scale values as learnable parameters updated via a surrogate gradient pathway, enabling task-driven optimization without requiring differentiation using discrete voxel indexing. Unlike prior approaches that adapt feature aggregation under fixed discretization, VoxAdapt allows voxel resolutions to be adjusted during training in response to the detection objectives and resource constraints. Experiments on the KITTI benchmark demonstrated that VoxAdapt enables robust detection of small, sparsely sampled objects that challenge fixed-scale methods while maintaining competitive performance on larger objects with minimal computational overhead. These results highlight the potential of learning adaptive geometric representations to support efficient and deployable 3D perception systems for real-time mobile and edge applications.