发表机构
Sungkyunkwan University; Horizon Robotics; Yonsei University（成均馆大学; 地平线机器人; 延世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对无姿态前馈3D重建中长序列因姿态漂移致性能下降等问题，提出用光线图引导耦合模块明确耦合几何与外观的协同框架及双频视点调度策略，提升了渲染和姿态估计性能，验证了几何-外观协同的关键作用。
AI 中文摘要
无姿态前馈3D高斯渲染（3DGS）是快速场景重建的强大范例。然而，由于累积相机姿态估计漂移，其在长图像序列中性能显著下降。我们重新审视长序列瓶颈，将姿态漂移视为限制重建质量的主要因素。基于SfM的伪地面真值姿态会引入传感器噪声，纯渲染监督会导致优化不稳定和局部最小值。为应对这些挑战，我们提出通过光线图引导耦合模块（RGC）明确耦合几何和外观的协同无姿态框架。具体而言，将高斯中心锚定到光线图诱导的几何上，在统一目标下联合优化RGB重建、光线图一致性和相机正则化，产生双向反馈循环。为进一步稳定跨宽时间范围的学习，引入双频视点调度策略。实验表明在渲染和姿态估计方面均有提升，消融研究验证了几何-外观协同是关键。
英文摘要
Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for fast scene reconstruction. However, its performance degrades significantly in long image sequences due to cumulative camera pose estimation drift, which propagates errors into geometric modeling and severely limits rendering fidelity. In this work, we revisit the long-sequence bottleneck and identify pose drift as the primary factor restricting reconstruction quality. Furthermore, while SfM-based pseudo ground-truth poses introduce sensor noise, purely rendering-based supervision often leads to optimization instability and local minima due to the entangled optimization of geometry and pose. To address the challenges, we propose a synergistic pose-free framework that explicitly couples geometry and appearance via a Raymap-Guided Coupling Module (RGC). Concretely, we anchor Gaussian centers to raymap-induced geometry and jointly optimize RGB reconstruction, raymap consistency, and camera regularization under a unified objective, yielding a bidirectional feedback loop: stronger geometry improves rendering, and appearance supervision in turn refines geometry and pose. To further stabilize learning across wide temporal ranges, we introduce a Dual-Frequency Viewpoint Scheduling strategy that combines easy-to-hard interval expansion with replay of short-interval pairs. Extensive experiments across in-domain and cross-domain datasets show consistent gains in both rendering and pose estimation, with notably improved robustness on long sequences. Ablation studies validate our central insight: explicitly designed geometry-appearance synergy is the key to scalable and drift-robust pose-free feed-forward 3D reconstruction. Project page: https://xiangyu1sun.github.io/NoDrift3R-project-page/
CommentsProject page: see https://xiangyu1sun.github.io/NoDrift3R-project-page/
