# 1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation

arXiv: https://arxiv.org/abs/2006.15505 · HTML: https://mlsanity.com/paper/2006.15505/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2203.08195/) (2022)
- [AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds](https://mlsanity.com/paper/2112.09205/) (2021)
- [LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion](https://mlsanity.com/paper/2303.03595/) (2023)
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://mlsanity.com/paper/2106.12735/) (2021)
- [Scalable Scene Flow from Point Clouds in the Real World](https://mlsanity.com/paper/2103.01306/) (2021)
- [Pseudo-labeling for Scalable 3D Object Detection](https://mlsanity.com/paper/2103.02093/) (2021)
- [DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds](https://mlsanity.com/paper/2306.06023/) (2023)
- [Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation](https://mlsanity.com/paper/2111.07971/) (2021)
- [HOPE: Hierarchical Spatial-temporal Network for Occupancy Flow Prediction](https://mlsanity.com/paper/2206.10118/) (2022)
- [PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection](https://mlsanity.com/paper/2211.08055/) (2022)
- [Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness](https://mlsanity.com/paper/2107.14342/) (2021)
- [A two-stage data association approach for 3D Multi-object Tracking](https://mlsanity.com/paper/2101.08684/) (2021)
- [VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation](https://mlsanity.com/paper/2411.13186/) (2024)
- [AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer](https://mlsanity.com/paper/2402.07680/) (2024)
- [Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs](https://mlsanity.com/paper/2408.11207/) (2024)

## Abstract (arXiv)

In this technical report, we introduce our winning solution "HorizonLiDAR3D" for the 3D detection track and the domain adaptation track in Waymo Open Dataset Challenge at CVPR 2020. Many existing 3D object detectors include prior-based anchor box design to account for different scales and aspect ratios and classes of objects, which limits its capability of generalization to a different dataset or domain and requires post-processing (e.g. Non-Maximum Suppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors. AFDet serves as a strong baseline in our winning solution and significant improvements are made over this baseline during the challenges. Specifically, we design stronger networks and enhance the point cloud data using densification and point painting. To leverage camera information, we append/paint additional attributes to each point by projecting them to camera space and gathering image-based perception information. The final detection performance also benefits from model ensemble and Test-Time Augmentation (TTA) in both the 3D detection track and the domain adaptation track. Our solution achieves the 1st place with 77.11% mAPH/L2 and 69.49% mAPH/L2 respectively on the 3D detection track and the domain adaptation track.