Reliable change detection in indoor environments remains a challenge for autonomous robotic systems using 3D LiDAR. Existing methods often require manual annotation, computationally intensive architectures, or focus on outdoor scenes. This paper presents SPICD-Net, a lightweight Siamese PointNet framework for indoor 3D change detection trained exclusively on synthetically generated anomalies, eliminating manual labeling. The framework offers three deployment-oriented contributions: a three-class Siamese formulation separating no-change, changed, and geometrically inconsistent tile pairs; a pre-FPS anomaly injection strategy that aligns synthetic training with inference-time preprocessing; and a stochastic-gated Chamfer-statistics branch that complements learned embeddings with explicit geometric cues under consumer-grade hardware constraints. Evaluated on 14 controlled simulation experiments in an indoor corridor dataset, SPICD-Net achieved aggregated Precision = 0.86, Recall = 0.82, F1-score = 0.84, and Accuracy = 0.96, with zero false positives in the no-change baseline and mean inference time of 22.4 s for a 172-tile map on a single consumer GPU. Additional robustness experiments identified registration accuracy as the main operational prerequisite. A limited real-world validation in one unseen room (four scans, 67 tiles) achieved Precision = 0.583, Recall = 1.000, and F1 = 0.737.

This paper presents a dashboard-oriented visual analytics workflow for classification-related exploratory analysis based on Information Gain (IG), histogram segmentation, and complementary localized interpretation through the Precise Piecewise Correlation (PPC) method. The workflow is designed to support the construction of a primary dashboard view by prioritizing attributes with stronger relevance to the decision variable and inspecting their class-related behavior within segmented histogram intervals. Rather than introducing a new standalone feature-selection metric, this study formalizes how established analytical components can be integrated into a coherent dashboard framework for structured visual inspection. The proposed workflow was examined on three datasets from different application domains: the Iris dataset, an educational performance dataset, and an Oil and Gas dataset. Across these cases, IG-based prioritization identified attributes that provided clearer class-related structure in the primary dashboard view, while histogram segmentation supported interval-level interpretation of class concentration and overlap. A compact quantitative evaluation further showed that top-ranked IG subsets retained strong discriminative information under standard classification models, whereas lower-ranked subsets generally performed less favorably. Entropy-based segment analysis additionally indicated lower local class uncertainty for higher-ranked attributes. A small user study provided preliminary user-centered support for the interpretability and practical usefulness of the proposed dashboard structure. The results suggest that the proposed workflow can support dashboard-based inspection of class-related patterns across different contexts.