# FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds

arXiv: https://arxiv.org/abs/2011.10174 · HTML: https://mlsanity.com/paper/2011.10174/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

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

- [Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://mlsanity.com/paper/2107.14160/) (2021)
- [TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception](https://mlsanity.com/paper/2412.03054/) (2024)
- [JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data](https://mlsanity.com/paper/2503.08422/) (2025)
- [PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds](https://mlsanity.com/paper/2304.08591/) (2023)
- [Towards Transferable Multi-modal Perception Representation Learning for Autonomy: NeRF-Supervised Masked AutoEncoder](https://mlsanity.com/paper/2311.13750/) (2023)

## Abstract (arXiv)

Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typically data hungry, requiring a large amount of data to be labeled for training and evaluation. However, annotating this kind of data is very challenging due to the sparsity and irregularity of point clouds and more complex interaction involved in this procedure. To tackle this problem, we propose FLAVA, a systematic approach to minimizing human interaction in the annotation process. Specifically, we divide the annotation pipeline into four parts: find, localize, adjust and verify. In addition, we carefully design the UI for different stages of the annotation procedure, thus keeping the annotators to focus on the aspects that are most important to each stage. Furthermore, our system also greatly reduces the amount of interaction by introducing a light-weight yet effective mechanism to propagate the annotation results. Experimental results show that our method can remarkably accelerate the procedure and improve the annotation quality.