# It's All Around You: Range-Guided Cylindrical Network for 3D Object Detection

arXiv: https://arxiv.org/abs/2012.03121 · HTML: https://mlsanity.com/paper/2012.03121/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [PolarFormer: Multi-camera 3D Object Detection with Polar Transformer](https://mlsanity.com/paper/2206.15398/) (2022)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars](https://mlsanity.com/paper/2106.07545/) (2021)
- [Perspective-Invariant 3D Object Detection](https://mlsanity.com/paper/2507.17665/) (2025)
- [Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds](https://mlsanity.com/paper/2207.12659/) (2022)
- [MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection](https://mlsanity.com/paper/2212.00442/) (2022)
- [Li3DeTr: A LiDAR based 3D Detection Transformer](https://mlsanity.com/paper/2210.15365/) (2022)
- [An Empirical Analysis of Range for 3D Object Detection](https://mlsanity.com/paper/2308.04054/) (2023)
- [Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge](https://mlsanity.com/paper/2309.09593/) (2023)
- [X-view: Non-egocentric Multi-View 3D Object Detector](https://mlsanity.com/paper/2103.13001/) (2021)
- [MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection](https://mlsanity.com/paper/2401.11718/) (2024)
- [Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation](https://mlsanity.com/paper/2111.09515/) (2021)
- [D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence](https://mlsanity.com/paper/2210.00087/) (2022)

## Abstract (arXiv)

Modern perception systems in the field of autonomous driving rely on 3D data analysis. LiDAR sensors are frequently used to acquire such data due to their increased resilience to different lighting conditions. Although rotating LiDAR scanners produce ring-shaped patterns in space, most networks analyze their data using an orthogonal voxel sampling strategy. This work presents a novel approach for analyzing 3D data produced by 360-degree depth scanners, utilizing a more suitable coordinate system, which is aligned with the scanning pattern. Furthermore, we introduce a novel notion of range-guided convolutions, adapting the receptive field by distance from the ego vehicle and the object's scale. Our network demonstrates powerful results on the nuScenes challenge, comparable to current state-of-the-art architectures. The backbone architecture introduced in this work can be easily integrated onto other pipelines as well.