GeoCurv-TTT: Geometry-Aware Deformation Restoration for 3D Test-Time Training

Recent 3D point cloud classification networks suffer severe performance degradation when encountering real-world distribution shifts. To address this, we propose GeoCurv-TTT, a novel geometry-aware Test-Time Training (TTT) framework based on deformation restoration. Unlike existing methods that rely on geometry-agnostic masking or severe skeletal abstraction, our approach selectively deforms low-curvature planar regions while strictly preserving high-curvature structural anchors. This curvature-guided physical displacement generates targeted out-of-distribution (OOD) samples, forcing the network to learn deeply robust, topology-aware representations during pre-training. At inference, GeoCurv-TTT adapts to novel environments through two strategies: Standard TTT, which utilizes a self-supervised restoration objective to adapt to isolated instances, and Online TTT, which achieves real-time, backpropagation-free adaptation across streaming data by exclusively updating batch normalization statistics. Extensive experiments on the ModelNet40-C, ShapeNet-C, and ScanObjectNN-C benchmarks demonstrate that GeoCurv-TTT establishes new state-of-the-art (SOTA) performance for 3D domain adaptation, outperforming existing methodologies in accuracy, computational efficiency, and adaptation time.