# Detecting and Mapping Trees in Unstructured Environments with a Stereo Camera and Pseudo-Lidar

arXiv: https://arxiv.org/abs/2103.15967 · HTML: https://mlsanity.com/paper/2103.15967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1906.06310/) (2019)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery](https://mlsanity.com/paper/1903.00268/) (2019)
- [RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar](https://mlsanity.com/paper/2004.03451/) (2020)
- [Fast, Autonomous Flight in GPS-Denied and Cluttered Environments](https://mlsanity.com/paper/1712.02052/) (2017)
- [Experiments in Fast, Autonomous, GPS-Denied Quadrotor Flight](https://mlsanity.com/paper/1806.07053/) (2018)

## Built on by (most cited first, 2 of 2)

- [StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks](https://mlsanity.com/paper/2209.08459/) (2022)
- [ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenes](https://mlsanity.com/paper/2609.36891/) (2026)

## Abstract (arXiv)

We present a method for detecting and mapping trees in noisy stereo camera point clouds, using a learned 3-D object detector. Inspired by recent advancements in 3-D object detection using a pseudo-lidar representation for stereo data, we train a PointRCNN detector to recognize trees in forest-like environments. We generate detector training data with a novel automatic labeling process that clusters a fused global point cloud. This process annotates large stereo point cloud training data sets with minimal user supervision, and unlike previous pseudo-lidar detection pipelines, requires no 3-D ground truth from other sensors such as lidar. Our mapping system additionally uses a Kalman filter to associate detections and consistently estimate the positions and sizes of trees. We collect a data set for tree detection consisting of 8680 stereo point clouds, and validate our method on an outdoors test sequence. Our results demonstrate robust tree recognition in noisy stereo data at ranges of up to 7 meters, on 720p resolution images from a Stereolabs ZED 2 camera. Code and data are available at https://github.com/brian-h-wang/pseudolidar-tree-detection.