Tracing Chains of Evidence: Cross-View Test-Time Adaptation for Point Cloud Part Segmentation

Point cloud part segmentation increasingly lifts multi-view predictions from 2D foundation models into 3D, reducing the need for costly point-level annotations. To assess the robustness of this paradigm, we establish, to the best of our knowledge, the first distribution-shift benchmark for 2D-to-3D part segmentation, spanning corruption, category, and sim-to-real shifts. Our evaluation reveals substantial performance degradation and exposes an inherent evidence-coupling challenge: multi-view predictions linked through shared 3D geometry can be complementary, ambiguous, or conflicting, hindering their reliable integration. To address this challenge, we propose **T**est-time **R**eliable **A**daptation via **C**hain-of-**E**vidence (**TRACE**), a novel TTA framework that recasts adaptation as discovering and reinforcing structural consensus within entangled evidence. Specifically, TRACE constructs a weighted evidence graph that jointly models semantic compatibility and 3D geometric consistency, organizing compatible cross-view predictions into evidence chains while routing ambiguous evidence to an explicit uncertainty state. To avoid the exponential cost of enumerating these chains, we derive an exact composition rule through mathematical induction that aggregates their support in closed form with linear complexity. Together, these designs move TTA beyond confidence-driven refinement of isolated predictions toward uncertainty-aware reasoning over structured cross-view consensus, enabling robust integration of complementary evidence. Extensive experiments across diverse domain shifts and base models demonstrate that TRACE achieves state-of-the-art performance, with an average improvement of +2.6% mIoU. Code will be publicly available.