发表机构
Nanjing University; TopXGun Robotics; Nanjing University of Posts and Telecommunications; Tsinghua University（南京大学; 拓攻机器人; 南京邮电大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对森林点云分割中结构不规则、遮挡严重和边界模糊等挑战，提出ForestQuery框架，通过边界不确定性建模和空间锚定语义查询增强，提升单木与语义分割性能，并在多个基准和真实数据集上验证了有效性。
AI 中文摘要
森林点云分割是细粒度三维森林场景理解的基础，但由于树木结构不规则、遮挡严重、密度变化大以及实例边界模糊，仍然具有挑战性。近年来基于查询的森林分割方法在统一语义和实例预测方面显示出潜力，但仍未能充分利用森林特有的空间结构并考虑边界不确定性。本文提出ForestQuery，一种面向统一森林点云分割的边界感知与空间锚定查询学习框架。ForestQuery通过两种互补设计增强实例和语义查询学习。具体而言，显式建模边界不确定性以指导可靠的实例查询构建，并通过自适应损失重新加权调节查询优化。同时，空间锚定语义查询增强（SA-SQE）引入可学习的3D锚点，编码森林垂直分层先验，以丰富语义查询的显式空间参考。我们在多个公共森林点云基准和一个自收集的带标注真实世界数据集上评估ForestQuery。大量实验表明，在不同森林场景中，单木分割和语义分割均取得一致改进。代码和数据可从此https URL公开获取。
英文摘要
Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structures, severe occlusions, density variations, and ambiguous instance boundaries. Recent query-based forest segmentation methods have shown promise for unified semantic and instance prediction, but they still insufficiently exploit forest-specific spatial structure and account for boundary uncertainty. In this paper, we propose ForestQuery, a boundary-aware and spatially anchored query learning framework for unified forest point cloud segmentation. ForestQuery enhances instance and semantic query learning through two complementary designs. Specifically, boundary uncertainty is explicitly modeled to guide reliable instance query construction and modulate query optimization through adaptive loss reweighting. Meanwhile, spatially anchored semantic query enhancement (SA-SQE) introduces learnable 3D anchors encoding forest vertical stratification priors to enrich semantic queries with explicit spatial references. We evaluate ForestQuery on multiple public forest point cloud benchmarks and a self-collected annotated real-world dataset. Extensive experiments demonstrate consistent improvements in both individual-tree segmentation and semantic segmentation across diverse forest scenes. Code and data are publicly available at https://zhan994.github.io/ForestQuery
