ORBIT：利用360度视频在野外基准测试SfM
ORBIT: Benchmarking SfM in the Wild with 360deg Video
为视觉研究者提供了一个基于360度全景视频构建的、具有挑战性的SfM相机位姿估计基准，可用于评估和推动在复杂相机运动和动态场景下的三维重建与定位方法。
原文依据 we introduce a new benchmark for evaluating camera pose estimation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论Structure-from-Motion（SfM）是三维感知的基石，然而当前方法在应用于涉及复杂相机运动或动态场景的复杂视频时常常失败。更糟糕的是，该领域缺乏针对此类困难场景的可靠真值基准，这使得难以衡量真实世界的进展，或 pinpoint 最需要改进的地方。为了填补这一空白，我们引入了一个新的基准来评估相机位姿估计。我们的关键见解是利用在线全景360度视频作为数据源，从中构建具有挑战性的片段，同时仍能实现稳健的真值轨迹恢复。这些视频的全景特性为跟踪相机运动提供了更丰富的视觉上下文，即使部分视图受到模糊、运动或动态物体的影响。通过在全景360度视频中跟踪相机运动，我们裁剪并重新投影选定的部分，生成透视视图片段作为我们的基准——ORBIT——一个包含100个视频片段的多样化集合。实验表明，COLMAP和其他最优的SfM方法在我们的基准上难以准确估计相机位置，这表明它仍然是一个具有挑战性的开放问题空间，供未来研究。因此，ORBIT提供了一个有价值的测试平台，研究人员可以在真正具有挑战性的真实世界SfM问题上进行有意义的竞争并衡量进展。
英文原文摘要 1398 字符
Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging camera motions or dynamic scenes.Compounding the problem, the field lacks reliable ground-truth benchmarks for such difficult scenarios, making it hard to gauge real-world progress, or pinpoint where improvements are most needed.To address this gap, we introduce a new benchmark for evaluating camera pose estimation.Our key insight is to leverage online panoramic 360deg as a source of data from which to construct challenging clips, while still enabling robust ground-truth trajectory recovery.The panoramic nature of these videos provides richer visual context for tracking camera motion, even when parts of the view are affected by blur, motion, or dynamic objects.By tracking camera motion across full 360deg videos, we crop and reproject selected portions to generate perspective-view clips that serve as our benchmark---ORBIT---a diverse collection of 100 video clips.Experiments show that COLMAP and other state-of-the-art SfM methods struggle to accurately estimate camera positions on our benchmark, indicating that it remains a challenging and open problem space for future research.As a result, ORBIT provides a valuable testbed where researchers can meaningfully compete and measure progress on truly challenging, real-world SfM problems.