Failure-Aware Adaptation for Transparent Objects in Feed-Forward 3D Reconstruction

Feed-forward 3D foundation models reconstruct scene geometry from a set of in- put views without per-scene optimization, yet remain unreliable for transparent objects. We analyze these failures and find that transparent-object errors concen- trate among a small fraction of high-error predictions and separate into two asym- metric modes, overestimation and underestimation. Overestimation is primar- ily associated with background leakage, whereas underestimation concentrates around concave structures, revealing distinct geometric characteristics between the two failure modes. Motivated by this asymmetry, we introduce a failure- aware parameter-efficient adaptation framework with two complementary mod- ules. Background-aware LoRA selectively adapts regions associated with back- ground leakage using background geometric cues. Curvature-Informed Corrector infers error directions from local geometric cues and corrects overestimation and underestimation through separate correction paths. Our method achieves state-of- the-art performance on depth estimation benchmarks for transparent objects while consistently improving reconstruction accuracy across different feed-forward 3D foundation models.