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PlantC2USeg: Cross-Scale Consistent Pre-Training for Few-Shot Unified Plant Point Cloud Segmentation

Paper recorded by Signals 4 on 2026-09-02 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.CV · 计算机视觉 · first seen 2026-09-03

Abstract

Modern crop breeding demands precise organ-level analysis for trait quantification, making plant point cloud segmentation (PPCS) increasingly important. However, conventional deep learning approaches rely heavily on densely annotated datasets that are labor-intensive to acquire. Unified PPCS adaptation from distribution-shifted examples with minimal additional training remains challenging. To addr

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#160 most recent of 237 cs.CV papers we have recorded · ↑ newer: Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Imag · ↓ older: MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video
Cite this page: PlantC2USeg: Cross-Scale Consistent Pre-Training for Few-Shot Unified Plant Point Cloud Segmentation: the #160 most recent of 237 cs.CV papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/plantc2useg-cross-scale-consistent-pre-training-for-few-shot-unified-plant-point.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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