Title: CountingFruit: Language-Guided 3D Fruit Counting with Semantic Gaussian Splatting Show Chinese title

Title: CountingFruit：语义高斯点云的语言引导三维水果计数

Abstract: Accurate 3D fruit counting in orchards is challenging due to heavy occlusion, semantic ambiguity between fruits and surrounding structures, and the high computational cost of volumetric reconstruction. Existing pipelines often rely on multi-view 2D segmentation and dense volumetric sampling, which lead to accumulated fusion errors and slow inference. We introduce FruitLangGS, a language-guided 3D fruit counting framework that reconstructs orchard-scale scenes using an adaptive-density Gaussian Splatting pipeline with radius-aware pruning and tile-based rasterization, enabling scalable 3D representation. During inference, compressed CLIP-aligned semantic vectors embedded in each Gaussian are filtered via a dual-threshold cosine similarity mechanism, retrieving Gaussians relevant to target prompts while suppressing common distractors (e.g., foliage), without requiring retraining or image-space masks. The selected Gaussians are then sampled into dense point clouds and clustered geometrically to estimate fruit instances, remaining robust under severe occlusion and viewpoint variation. Experiments on nine different orchard-scale datasets demonstrate that FruitLangGS consistently outperforms existing pipelines in instance counting recall, avoiding multi-view segmentation fusion errors and achieving up to 99.2\% recall on Fuji-SfM orchard dataset. Ablation studies further confirm that language-conditioned semantic embedding and dual-threshold prompt filtering are essential for suppressing distractors and improving counting accuracy under heavy occlusion. Beyond fruit counting, the same framework enables prompt-driven 3D semantic retrieval without retraining, highlighting the potential of language-guided 3D perception for scalable agricultural scene understanding. Show Chinese abstract

Abstract: 准确的果园中3D水果计数具有挑战性，这是由于严重的遮挡、水果与周围结构之间的语义模糊性以及体积重建的高计算成本。现有的流程通常依赖于多视角2D分割和密集的体积采样，这会导致累积的融合误差和缓慢的推理。我们引入了FruitLangGS，这是一种语言引导的3D水果计数框架，使用自适应密度的高斯点云管道进行果园规模场景的重建，结合半径感知的剪枝和基于图块的光栅化，实现可扩展的3D表示。在推理过程中，每个高斯中嵌入的压缩CLIP对齐语义向量通过双阈值余弦相似度机制进行过滤，检索与目标提示相关的高斯点，同时抑制常见的干扰项（例如，树叶），而无需重新训练或图像空间掩码。然后将选定的高斯点采样为密集点云，并进行几何聚类以估计水果实例，在严重遮挡和视角变化下保持鲁棒性。在九个不同的果园规模数据集上的实验表明，FruitLangGS在实例计数召回率方面始终优于现有流程，避免了多视角分割融合误差，并在Fuji-SfM果园数据集上实现了高达99.2%的召回率。消融研究进一步证实，语言条件语义嵌入和双阈值提示过滤对于在严重遮挡下抑制干扰项和提高计数准确性至关重要。除了水果计数之外，同一框架还能在不重新训练的情况下实现提示驱动的3D语义检索，突显了语言引导的3D感知在可扩展农业场景理解中的潜力。