| name | anytime-lidar-resolution-scaling-object-detection |
| description | Anytime computing method for LiDAR-based 3D object detection in cyber-physical systems. Multi-resolution inference with single DNN model, deadline-aware scheduler predicts execution time for all resolutions. Deployed in simulated autonomous driving with collision-free navigation. Use when working with anytime-computing, lidar-detection, input-resolution-scaling. |
On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection
Description
Methodology from arXiv:2607.08391 (Ahmet Soyyigit et al., July 2026). Anytime computing method for LiDAR-based 3D object detection in cyber-physical systems. Multi-resolution inference with single DNN model, deadline-aware scheduler predicts execution time for all resolutions. Deployed in simulated autonomous driving with collision-free navigation.
arXiv: 2607.08391
Categories: cs.RO, cs.LG
Authors: Ahmet Soyyigit, Shuochao Yao, Heechul Yun
Activation Keywords
anytime LiDAR detection, input resolution scaling, multi-resolution inference, deadline-aware scheduler, cyber-physical anytime computing, point cloud resolution, autonomous driving LiDAR, real-time object detection
Core Methodology
Problem
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. Our memory-efficient approach requires only a single DNN model. We introduce a deadline-aware scheduler that selects the highest possible resolution by accurately predicting execution time for all possible resolutions at runtime.
Key Contributions
- Novel framework addressing limitations in anytime computing
- Practical evaluation demonstrating significant improvements
- Scalable design with real-world applicability
Technical Highlights
- Architecture-preserving and efficient
- Evaluated on standard benchmarks
- Demonstrates state-of-the-art or near-SOTA performance
Implementation Guide
Step 1: Understand the Approach
pass
Step 2: Integration Points
- Can be integrated with existing pipelines
- Modular design allows for component-level adoption
- Configuration parameters for domain-specific tuning
Step 3: Evaluation
- Benchmark on standard datasets
- Compare with baseline methods
- Measure key metrics: accuracy, efficiency, scalability
Common Pitfalls
Pitfall 1: Resource Requirements
Issue: Method may require significant computational resources.
Fix: Start with smaller-scale experiments before full deployment.
Pitfall 2: Domain Transfer
Issue: Performance may vary across different domains.
Fix: Validate on domain-specific data before production use.
When to Use
- When anytime computing is needed
- For applications requiring lidar detection
- When standard approaches have limitations in input resolution scaling
References
- arXiv:2607.08391 - "On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection"
- Categories: cs.RO, cs.LG
- Published: July 2026
