发表机构
Zhejiang Sci-Tech University; Tsinghua University; CardioCloud Medical Technology (Beijing) Co., Ltd.; Lishui University; Zhejiang University; Jiaxing University（浙江理工大学; 清华大学; 心云医疗科技（北京）有限公司; 丽水学院; 浙江大学; 嘉兴大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无COLMAP三维高斯泼溅中渐进式跟踪的误差累积问题，提出统一可靠性调控框架，通过双向循环一致性在前向传播与回顾修正中调控轨迹，显著提升轨迹精度与渲染质量。
AI 中文摘要
无COLMAP的三维高斯泼溅（3DGS）绕过了计算昂贵的运动恢复结构（SfM）流程，然而渐进式相机位姿跟踪从根本上容易受到误差累积的影响——早期的成对跟踪不准确既会破坏后续帧的初始化，也会在场景表示中永久固化。我们不依赖重型的外部神经先验，也不通过孤立的启发式修复来处理渐进式跟踪，而是提出了一种统一的可靠性调控轨迹优化框架，用于渐进式无COLMAP的3DGS。该框架的核心是建立一种内在的、自监督的双向循环一致性机制，在两个互补的时间尺度上系统地调控渐进式相机轨迹估计：（1）前向运动传播，其中在线可靠性信号自适应地门控刚体运动的一阶运动学热启动进入后续的成对配准，提供有信息的方向搜索先验，同时安全地拦截不可信的过渡；（2）回顾性轨迹修正，其中相同的可靠性信号在相邻相机位姿的滑动窗口内动态加权相对位姿一致性约束。通过统一的可靠性调控器同时控制前瞻性状态初始化和回顾性轨迹整合，我们的自包含框架无需外部先验或离线预处理即可解决渐进式漂移。在Tanks and Temples和CO3D-V2基准上的广泛评估表明，我们的方法显著提高了相机轨迹精度和新视角渲染质量，优于现有的无位姿基线。代码可在以下网址获取：https URL。
英文摘要
COLMAP-free 3D Gaussian Splatting (3DGS) bypasses computationally expensive structure-from-motion (SfM) pipelines, yet progressive camera pose tracking remains fundamentally vulnerable to error compounding---early pairwise tracking inaccuracies both corrupt subsequent frame initializations and remain permanently frozen in the scene representation. Rather than relying on heavyweight external neural priors or treating progressive tracking through isolated heuristic fixes, we propose a unified reliability-regulated trajectory optimization framework for progressive COLMAP-free 3DGS. At its core, our framework establishes an intrinsic, self-supervised bidirectional cycle-consistency mechanism that systematically regulates progressive camera trajectory estimation across two complementary temporal horizons: (1) Forward Motion Propagation, where the online reliability signal adaptively gates first-order kinematic warm-starts of rigid motion into upcoming pairwise registrations, supplying informed directional search priors while safely intercepting untrusted transitions; and (2) Retrospective Trajectory Correction, where the same reliability signal dynamically weights relative-pose consistency constraints within a sliding window of neighboring camera poses. By governing both prospective state initialization and retrospective trajectory consolidation through a unified reliability regulator, our self-contained framework resolves progressive drift without external priors or offline preprocessing. Extensive evaluations on Tanks and Temples and CO3D-V2 benchmarks show that our method substantially improves camera trajectory accuracy and novel-view rendering quality, outperforming existing unposed baselines. Code is available at https://github.com/Zijian1026/RRTO-CF3DGS.
