# Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

arXiv: https://arxiv.org/abs/1904.07537 · HTML: https://mlsanity.com/paper/1904.07537/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 30 of 35)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://mlsanity.com/paper/2203.11139/) (2022)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2108.06455/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud](https://mlsanity.com/paper/2108.05630/) (2021)
- [AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving](https://mlsanity.com/paper/2302.08646/) (2023)
- [Real-time 3D Single Object Tracking with Transformer](https://mlsanity.com/paper/2209.00860/) (2022)
- [OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detection](https://mlsanity.com/paper/2312.08876/) (2023)
- [TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking](https://mlsanity.com/paper/2101.04206/) (2021)
- [SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization](https://mlsanity.com/paper/2503.14558/) (2025)
- [DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation](https://mlsanity.com/paper/2404.04629/) (2024)
- [MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2402.11677/) (2024)
- [Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension](https://mlsanity.com/paper/2403.03532/) (2024)
- [Object Detection in Autonomous Vehicles: Status and Open Challenges](https://mlsanity.com/paper/2201.07706/) (2022)
- [Density-invariant Features for Distant Point Cloud Registration](https://mlsanity.com/paper/2307.09788/) (2023)
- [Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration](https://mlsanity.com/paper/2105.14683/) (2021)
- [Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild](https://mlsanity.com/paper/2111.12728/) (2021)
- [Sequential Point Clouds: A Survey](https://mlsanity.com/paper/2204.09337/) (2022)
- [3D Multiple Object Tracking on Autonomous Driving: A Literature Review](https://mlsanity.com/paper/2309.15411/) (2023)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [Uncertainty-Aware AB3DMOT by Variational 3D Object Detection](https://mlsanity.com/paper/2302.05923/) (2023)
- [Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs](https://mlsanity.com/paper/2303.09915/) (2023)
- [Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR](https://mlsanity.com/paper/2508.00744/) (2025)
- [L3DR: 3D-aware LiDAR Diffusion and Rectification](https://mlsanity.com/paper/2602.19064/) (2026)
- [DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation](https://mlsanity.com/paper/2203.02157/) (2022)
- [IC-FPS: Instance-Centroid Faster Point Sampling Module for 3D Point-base Object Detection](https://mlsanity.com/paper/2303.17921/) (2023)
- [Sports Analysis and VR Viewing System Based on Player Tracking and Pose Estimation with Multimodal and Multiview Sensors](https://mlsanity.com/paper/2405.01112/) (2024)
- [Multi-level and multi-modal feature fusion for accurate 3D object detection in Connected and Automated Vehicles](https://mlsanity.com/paper/2212.07560/) (2022)
- [Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations](https://mlsanity.com/paper/2308.03276/) (2023)

## Abstract (arXiv)

Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robotics. In this work we present a novel fusion of neural network based state-of-the-art 3D detector and visual semantic segmentation in the context of autonomous driving. Additionally, we introduce Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable evaluation metric for comparison of object detections, which speeds up our inference time up to 20% and halves training time. On top, we apply state-of-the-art online multi target feature tracking on the object measurements to further increase accuracy and robustness utilizing temporal information. Our experiments on KITTI show that we achieve same results as state-of-the-art in all related categories, while maintaining the performance and accuracy trade-off and still run in real-time. Furthermore, our model is the first one that fuses visual semantic with 3D object detection.