Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild Video

Understanding camera motion is a fundamental problem inembodied perception and 3D scene understanding. While visual methodshave advanced rapidly, they often struggle under visually degraded condi-tions such as motion blur or occlusions. In this work, we show that passivescene sounds provide cues complementary to vision for relative camerapose estimation for in-the-wild videos. We introduce a simple but effec-tive audio-visual framework that integrates direction-of-arrival (DOA)spectra and binauralized embeddings into a state-of-the-art vision-onlypose estimation model. Our results on two large datasets show consistentgains over strong visual baselines, plus robustness when the visual infor-mation is corrupted. To our knowledge, this represents the first work tosuccessfully leverage audio for relative camera pose estimation in real-world videos, and it establishes incidental, everyday audio as an unex-pected but promising signal for a classic spatial challenge.