arXiv:2607.08391v1 Announce Type: cross Abstract: 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 n
The increasing complexity and computational demands of AI models for real-time applications necessitate more efficient processing methods, especially in resource-constrained environments.
This development allows AI systems, especially in robotics and autonomous vehicles, to dynamically balance performance and resource utilization, enhancing adaptability and operational efficiency.
Deep neural networks processing LiDAR data can now adjust their input resolution on the fly, leading to more flexible and efficient deployment in dynamic cyber-physical systems.
- · Robotics companies
- · Autonomous vehicle developers
- · Edge AI hardware manufacturers
- · Logistics and industrial automation
- · Manufacturers of static-resolution sensors
- · Inefficient AI model developers
- · Systems with high latency requirements
Improved performance and energy efficiency of AI systems in real-time applications involving LiDAR data.
Accelerated adoption of advanced robotics and autonomous systems due to enhanced operational flexibility and reduced computational overhead.
Increased integration of AI into critical infrastructure and sensitive operations, pushing demand for robust, adaptable AI compute and the underlying supply chain.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG