Memory Matters: A Diagnostic Memory Benchmark for Online 3D Reconstruction

Feed-forward online 3D reconstruction is a challenging long-context reasoning problem, where large geometry models rely on memory mechanisms to retain historical information and preserve reconstruction accuracy. However, existing benchmarks mainly measure final reconstruction quality, offering limited insight into what memory retains, when it updates, how it handles unreliable inputs, and how efficiently it compresses scene history. We introduce Memo3R, a diagnostic benchmark that evaluates memory in online 3D reconstruction along four dimensions: persistence, plasticity, robustness, and compactness. For each dimension, we design controlled causal protocols and diagnostic metrics that isolate memory behavior. Benchmarking representative implicit- and explicit-memory methods across indoor and outdoor scenes reveals memory-specific trade-offs beyond conventional reconstruction metrics, providing practical guidance for future long-context 3D reconstruction models.