Polar Hierarchical Mamba: Towards Streaming LiDAR Object Detection with Point Clouds as Egocentric Sequences

• URL: http://arxiv.org/abs/2506.06944v1
• Date: Sat, 07 Jun 2025 22:53:50 GMT
• Title: Polar Hierarchical Mamba: Towards Streaming LiDAR Object Detection with Point Clouds as Egocentric Sequences
• Authors: Mellon M. Zhang, Glen Chou, Saibal Mukhopadhyay,
• Abstract summary: Real-time object detection is essential for autonomous vehicles, where real-time perception requires low latency and high throughput.<n>Recent Mamba-based state space models (SSMs) have shown promise for LiDAR perception, but only in the full-scan setting.<n>We propose Polar Hierarchical Mamba, a novel SSM architecture designed for polar-coordinate streaming LiDAR.
• Score: 12.420075828114326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient object detection is essential for autonomous vehicles, where real-time perception requires low latency and high throughput. LiDAR sensors provide robust depth information, but conventional methods process full 360{\deg} scans in a single pass, introducing significant delay. Streaming approaches address this by sequentially processing partial scans in the native polar coordinate system, yet they rely on translation-invariant convolutions that are misaligned with polar geometry -- resulting in degraded performance or requiring complex distortion mitigation. Recent Mamba-based state space models (SSMs) have shown promise for LiDAR perception, but only in the full-scan setting, relying on geometric serialization and positional embeddings that are memory-intensive and ill-suited to streaming. We propose Polar Hierarchical Mamba (PHiM), a novel SSM architecture designed for polar-coordinate streaming LiDAR. PHiM uses local bidirectional Mamba blocks for intra-sector spatial encoding and a global forward Mamba for inter-sector temporal modeling, replacing convolutions and positional encodings with distortion-aware, dimensionally-decomposed operations. PHiM sets a new state-of-the-art among streaming detectors on the Waymo Open Dataset, outperforming the previous best by 10\% and matching full-scan baselines at twice the throughput. Code will be available at https://github.com/meilongzhang/Polar-Hierarchical-Mamba .

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