Exploiting Vision Language Model for Training-Free 3D Point Cloud Understanding via Improved Graph Score Propagation

3D point cloud understanding, e.g. out-of-distribution detection and recognition, remains a challenge, particularly in applications where safe and robust perception is critical. While recent 3D VLMs enable direct recognition via text prompts, their predictions are often unstable when the test distribution shifts or when only a handful of labeled samples are available. In this paper, we present GSP++, a graph-based inference framework that exploits the manifold structure of test-time point clouds to refine VLM scores without additional training. GSP++ constructs a graph over class prototypes, optional few-shot samples, and unlabeled test instances, and performs log-scaled score propagation to improve robustness across different dataset sizes while keeping computation efficient. The framework enables unified 3D OOD detection and classification. Moreover, we introduce a self-training strategy that selects high-confidence positive and negative samples and assigns them calibrated pseudo scores to further stabilize propagation. To enable practical deployment, we introduce dynamic graph construction and maintenance strategies that efficiently manage computation and memory, enabling scalable 3D understanding under both transductive and incremental inference. Extensive experiments on six datasets show that GSP++ not only reproduces and strengthens the full suite of OOD detection evaluations from GSP, a prior conference version, but also consistently improves zero-shot and few-shot 3D classification over strong VLM baselines. The code is available on https://github.com/handsome999KK/GSP.