Calibrating Panoramic Depth Estimation for Practical Localization and Mapping

TL;DR

Panoramic depth estimation enables rich 3D context for localization and mapping but suffers from domain shifts and ambiguous absolute depths. The authors propose a light-weight, test-time calibration using geometric consistency, leveraging panorama synthesis and a joint loss $L = L_S + L_C + L_N$ along with panorama stretching and data augmentation to adapt online or offline. The method improves depth accuracy and yields strong gains in downstream tasks, including map-free localization and robot navigation, validating its practicality in panorama-based vision systems. By obviating large-scale retraining and enabling rapid adaptation to new environments, this calibration broadens the applicability of panoramic depth sensing in real-world localization and mapping tasks.

Abstract

The absolute depth values of surrounding environments provide crucial cues for various assistive technologies, such as localization, navigation, and 3D structure estimation. We propose that accurate depth estimated from panoramic images can serve as a powerful and light-weight input for a wide range of downstream tasks requiring 3D information. While panoramic images can easily capture the surrounding context from commodity devices, the estimated depth shares the limitations of conventional image-based depth estimation; the performance deteriorates under large domain shifts and the absolute values are still ambiguous to infer from 2D observations. By taking advantage of the holistic view, we mitigate such effects in a self-supervised way and fine-tune the network with geometric consistency during the test phase. Specifically, we construct a 3D point cloud from the current depth prediction and project the point cloud at various viewpoints or apply stretches on the current input image to generate synthetic panoramas. Then we minimize the discrepancy of the 3D structure estimated from synthetic images without collecting additional data. We empirically evaluate our method in robot navigation and map-free localization where our method shows large performance enhancements. Our calibration method can therefore widen the applicability under various external conditions, serving as a key component for practical panorama-based machine vision systems. Code is available through the following link: \url{https://github.com/82magnolia/panoramic-depth-calibration}.

Figures (9)

• Figure 1: Motivation and overview of our approach. Panoramic perception enables efficient navigation due to the large field of view (top). Nevertheless, the performance drops due to the gaps between the training dataset with upright cameras in medium-sized rooms and the deployment scenarios with limited data and various domain shifts. The proposed solution suggests test-time training using geometric consistency to mitigate the gap (bottom).
• Figure 2: Description of the proposed test-time training objectives.
• Figure 3: Robot agent with panoramic perception (top) and application of panoramic depth calibration on robot navigation task (bottom).
• Figure 4: Description of map-free localization task (top) and its test-time adaptation pipeline (bottom).
• Figure 5: Visualization of domain changes.