anytime-lidar-resolution-scaling-object-detection

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

# Core concept: anytime lidar resolution scaling object detection
# This methodology provides a framework for anytime computing
# Reference: arXiv:2607.08391
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