Reliable long-range perception is a fundamental challenge for autonomous vehicles, demanding both high semantic understanding and precise distance estimation. In this presentation, we introduce SpotNet: a fast, single-stage, image-centric yet LiDAR-anchored approach to long-range 3D object detection. Based on our recently published research, SpotNet efficiently combines image and LiDAR data to deliver high accuracy with very sparse LiDAR input, achieving superior scalability compared to traditional BEV (bird’s-eye-view) methods. By anchoring predictions directly to LiDAR points, SpotNet bypasses explicit distance regression, enabling seamless transfer across image resolutions without retraining. This architecture highlights a new direction in efficient, high-performance sensor fusion, perfectly aligned with the needs of scalable, production-ready autonomous systems.
In this session, you will learn more about:
- How combining image-centric processing with LiDAR anchoring results in long-range perception that scales independently of distance
- How SpotNet maintains strong 3D detection performance even with minimal LiDAR data, making it practical for real-world deployment
- How the architecture’s design enables straightforward upgrading from lower to higher resolution cameras without the cost and complexity of retraining
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