Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic
This paper addresses the challenge of perception in dense, unstructured urban traffic for autonomous driving by proposing a 360-degree LiDAR perception framework that combines sector-wise panoramic processing with rotation-equivariant sparse convolutions. Evaluated on a custom dataset from diverse Indian traffic conditions, the framework achieves strong detection for cars (92.02/90.51), buses (80.53/76.34), and trucks (78.59/74.16), while lower performance on pedestrians (67.45/61.02), cyclists (73.21/69.54), and motorcyclists (71.20/68.13) highlights the difficulty of detecting smaller, variable road users.
[2606.07626] Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic
[Submitted on 30 May 2026]
Title:Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic
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Abstract:Perception in dense, unstructured urban traffic remains a major challenge for autonomous driving because of the wide variety of road users, frequent occlusions, irregular motion patterns, and the lack of standardized road layouts. Although recent LiDAR based 3D object detectors have shown strong performance in structured driving scenarios, most are developed and evaluated for limited field of view settings, and their behavior under full surround 360-degree sensing is still not well understood. This paper studies a 360-degree LiDAR perception pipeline for autonomous driving, with particular attention to panoramic sensing, azimuthal sector wise spatial processing, and transformation equivariant feature extraction in complex urban scenes. The paper presents a practical 360-degree perception framework that combines sector wise panoramic processing with rotation equivariant sparse convolutions and evaluates its behavior on a custom Ouster OS0 LiDAR dataset collected across diverse Indian urban traffic conditions. The results show generally stable detection across several object classes, with the strongest performance for cars at 92.02/90.51, buses at 80.53/76.34, and trucks at 78.59/74.16, while lower scores for pedestrians at 67.45/61.02, cyclists at 73.21/69.54, and motorcyclists at 71.20/68.13 reflect the greater difficulty of detecting smaller and more variable road users in dense urban scenes.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07626 [cs.CV]
(or arXiv:2606.07626v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07626
arXiv-issued DOI via DataCite
Submission history
From: Pranav Darshan [view email] [v1] Sat, 30 May 2026 15:30:58 UTC (1,476 KB)
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