Papers › Online Segmentation of LiDAR Sequences: Dataset and Algorithm
Online Segmentation of LiDAR Sequences: Dataset and Algorithm
Romain Loiseau, Mathieu Aubry, Loïc Landrieu
Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles. However, most semantic datasets and algorithms used for LiDAR sequence segmentation operate on 360° frames, causing an acquisition latency incompatible with real-time applications. To address this issue, we first introduce HelixNet, a $10$ billion point dataset with fine-grained labels, timestamps, and sensor rotation information necessary to accurately assess the real-time readiness of segmentation algorithms. Second, we propose Helix4D, a compact and efficient spatio-temporal transformer architecture specifically designed for rotating LiDAR sequences. Helix4D operates on acquisition slices corresponding to a fraction of a full sensor rotation, significantly reducing the total latency. Helix4D reaches accuracy on par with the best segmentation algorithms on HelixNet and SemanticKITTI with a reduction of over 5× in terms of latency and 50× in model size. The code and data are available at: https://romainloiseau.fr/helixnet
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Real-Time Semantic Segmentation | HelixNet | Helix4D | Inference Time (ms) (1/5 rotation) | 19 | #1 of 1 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | HelixNet | Helix4D | mIoU (1/5 rotation) | 78.7 | #1 of 1 | Archive leaderboard | report |
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