InFlux: A Benchmark for Self-Calibration of Dynamic Intrinsics of Video Cameras
This addresses a bottleneck for researchers in 3D computer vision by providing a more comprehensive benchmark for evaluating methods that handle dynamic camera intrinsics, though it is incremental as it builds on prior calibration tools.
The paper tackles the problem of lacking benchmarks for dynamic camera intrinsics in videos by introducing InFlux, a real-world benchmark with per-frame ground truth annotations, resulting in 143K+ annotated frames from 386 videos that capture diverse intrinsic variations.
Accurately tracking camera intrinsics is crucial for achieving 3D understanding from 2D video. However, most 3D algorithms assume that camera intrinsics stay constant throughout a video, which is often not true for many real-world in-the-wild videos. A major obstacle in this field is a lack of dynamic camera intrinsics benchmarks--existing benchmarks typically offer limited diversity in scene content and intrinsics variation, and none provide per-frame intrinsic changes for consecutive video frames. In this paper, we present Intrinsics in Flux (InFlux), a real-world benchmark that provides per-frame ground truth intrinsics annotations for videos with dynamic intrinsics. Compared to prior benchmarks, InFlux captures a wider range of intrinsic variations and scene diversity, featuring 143K+ annotated frames from 386 high-resolution indoor and outdoor videos with dynamic camera intrinsics. To ensure accurate per-frame intrinsics, we build a comprehensive lookup table of calibration experiments and extend the Kalibr toolbox to improve its accuracy and robustness. Using our benchmark, we evaluate existing baseline methods for predicting camera intrinsics and find that most struggle to achieve accurate predictions on videos with dynamic intrinsics. For the dataset, code, videos, and submission, please visit https://influx.cs.princeton.edu/.