GeoCond: A Conditioning-Aware Reliability Adapter for Feed-Forward 3D Reconstruction

2026-09-16

AI Summary

本文针对3D重建模型在特定条件下的失效问题，提出了GeoCond方法，通过预测几何结构的不确定性及精修门控来提高模型的可靠性。

Abstract

Feed-forward 3D foundation models such as VGGT predict cameras, depth, and point maps in a single pass, but can fail silently under low overlap, low parallax, and extreme relative rotation. Stratified analyses over these factors show that these failures are governed by geometric conditioning and are poorly captured by native aleatoric confidence. We introduce GeoCond, a lightweight reliability adapter for frozen feed-forward 3D backbones. GeoCond reads the backbone's predicted geometry and outputs pose-level uncertainty and a refinement gate. During training, it can be supervised by frame-permutation orbit variance, ground-truth pose error when labels are available, or cycle residuals from unlabelled independent pose graphs. At inference, the default head requires only one backbone pass and a small MLP. On VGGT, GeoCond improves out-of-distribution (OOD) AUSE (area under the sparsification-error curve; lower is better) from $0.32$ to $0.20$ over native confidence, transfers zero-shot to outdoor extreme-view scenes, and avoids the collapse caused by applying bundle adjustment uniformly. Across multiple backbones, cycle-distilled variants provide a ground-truth-free adaptation route, including cases where permutation variance vanishes on equivariant models. The same reliability signal supports gated refinement, pose-graph weighting, calibration, curation, and capture decisions. Reliable feed-forward 3D reconstruction requires not only predicting geometry, but also knowing when that geometry should be trusted.

Problem

• feed-forward 3D reconstruction
• geometric conditioning
• reliability
• uncertainty estimation
• out-of-distribution

Innovation

• GeoCond
• reliability adapter
• feed-forward 3D reconstruction
• geometric conditioning
• pose-level uncertainty