Signals 4 · free daily AI digest

The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds

Paper recorded by Signals 4 on 2026-09-18 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.CV · 计算机视觉 · first seen 2026-09-21

Abstract

Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomass estimation. Existing segmentation methods typically exclude radiometric features (e.g., intensity, return number) to maximize cross-sensor compatibility. We challenge this design choice by evaluating cross-site and cross-platform generalization: t

Read on arXiv →

#22 most recent of 270 cs.CV papers we have recorded · ↑ newer: Info3R: Information-Adaptive Test-Time Training for 3D Reconstruction · ↓ older: Catena: A Comprehensive Software Suite for Large-Scale Connectomics
Cite this page: The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds: the #22 most recent of 270 cs.CV papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/the-role-of-radiometric-features-in-cross-site-leaf-wood-segmentation-of-lidar-p.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions