# PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection

arXiv: https://arxiv.org/abs/2012.09418 · HTML: https://mlsanity.com/paper/2012.09418/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding](https://mlsanity.com/paper/2312.02244/) (2023)
- [Semi-supervised 3D Object Detection via Temporal Graph Neural Networks](https://mlsanity.com/paper/2202.00182/) (2022)
- [ImpDet: Exploring Implicit Fields for 3D Object Detection](https://mlsanity.com/paper/2203.17240/) (2022)
- [CVFNet: Real-time 3D Object Detection by Learning Cross View Features](https://mlsanity.com/paper/2203.06585/) (2022)
- [Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation](https://mlsanity.com/paper/2111.09515/) (2021)

## Abstract (arXiv)

Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure. Semantics come from the appearance and context of objects to the sensor, while geometric structure is the actual 3D shape of point clouds. Most detectors on LiDAR point clouds focus only on analyzing the geometric structure of objects in real 3D space. Unlike previous works, we propose to learn both semantic feature and geometric structure via a unified multi-view framework. Our method exploits the nature of LiDAR scans -- 2D range images, and applies well-studied 2D convolutions to extract semantic features. By fusing semantic and geometric features, our method outperforms state-of-the-art approaches in all categories by a large margin. The methodology of combining semantic and geometric features provides a unique perspective of looking at the problems in real-world 3D point cloud detection.