Search Results for author: Binbin Xiang

Found 5 papers, 3 papers with code

SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

no code implementations28 Jan 2024 Maciej Wielgosz, Stefano Puliti, Binbin Xiang, Konrad Schindler, Rasmus Astrup

In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests.

Instance Segmentation Segmentation +1

Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning

1 code implementation22 Dec 2023 Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni, Torben Peters, Stefano Puliti, Rasmus Astrup, Konrad Schindler

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services.

Segmentation

Towards accurate instance segmentation in large-scale LiDAR point clouds

1 code implementation6 Jul 2023 Binbin Xiang, Torben Peters, Theodora Kontogianni, Frawa Vetterli, Stefano Puliti, Rasmus Astrup, Konrad Schindler

Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances.

Clustering Instance Segmentation +5

A Review of Panoptic Segmentation for Mobile Mapping Point Clouds

1 code implementation27 Apr 2023 Binbin Xiang, Yuanwen Yue, Torben Peters, Konrad Schindler

Moreover, a modular pipeline is set up to perform comprehensive, systematic experiments to assess the state of panoptic segmentation in the context of street mapping.

Instance Segmentation Panoptic Segmentation +2

Multiple Combined Constraints for Image Stitching

no code implementations18 Sep 2018 Kai Chen, Jingmin Tu, Binbin Xiang, Li Li, Jian Yao

In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary.

Image Stitching

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