Unmanned aerial vehicle (UAV) scanning and mobile laser scanning provide detailed three-dimensional observations for forest inventory and structural monitoring for which point-wise separation of ground, understory, woody components, and foliage is fundamental. Modern segmentation pipelines can produce multiple predictions from different feature configurations, training objectives, random seeds, or network architectures. However, equal and fixed-weight fusion implicitly assume stable source quality, although the relative reliability of these predictions can vary across semantic classes and sensing conditions, leading to error propagation and negative transfer. We therefore formulate multi-source prediction integration as a calibration-supported class-wise reliability decision problem and propose the unified class-wise reliability gate (UCRG). The UCRG measures global and class-wise source reliability from labeled calibration data, uses shrinkage to stabilize class-specific estimates when labels are scarce, and conditionally performs soft fusion or hard source selection according to the reliability gap. An optional calibration-guided confusion-pair refiner further targets residual ambiguity between semantically similar classes. All the reliability estimation and parameter selection processes are performed without query labels. Experiments on FOR-instance, EvoMLS, and Lin3D cover UAV and mobile laser scanning together with cross-dataset transfer. Using one shared parameterization across the three primary protocols, the UCRG obtains 70.68% mIoU and records two negative-transfer episodes across the six evaluated episodes compared with six for fixed-weight fusion. When the gate parameters are selected solely from the available calibration support in each deployment, the UCRG reaches 59.20% mean mIoU in the PTv3-augmented Lin3D candidate pool. These results show that reliability-guided decisions can exploit class-dependent complementarity across heterogeneous frozen predictors and sensing conditions.

Recent 3D Transformers have become a dominant framework for point-cloud segmentation by modeling spatial context in sparse 3D scenes. However, geometry and color alone provide limited high-level semantic cues, especially for cluttered boundary regions, visually similar objects, and long-tail categories. To address this issue, we propose a segmentation framework guided by Contrastive Language–Image Pre-training (CLIP) that enriches sparse 3D tokens with vision–language semantic priors. Specifically, dense CLIP features extracted from multi-view RGB images are projected onto 3D points through visibility-aware alignment and view pooling, and are fused with relative geometric offsets and color cues to form semantically aware sparse voxel tokens. To better exploit the aligned CLIP semantics during local token interactions, we build on contextual relative signal encoding (cRSE) and introduce a decoupled CLIP-induced semantic residual that forms semantic-geometric attention biases for local window attention. We further adapt block-wise online softmax computation to generate and consume these biases on the fly. Experiments on ScanNet, ScanNet200, and S3DIS demonstrate competitive segmentation performance, improved instance-level discrimination, and a 25.7% reduction in peak online 3D-stage training memory compared with the materialized attention implementation when cached CLIP features are used.