Depth2Pose: A Pose-Based Benchmark for Monocular Depth Estimation without Ground-Truth Depth
Researchers have introduced Depth2Pose, a new benchmark for evaluating monocular depth estimation models. This framework assesses depth quality based on the accuracy of camera pose estimation, a more practical metric for downstream tasks like visual localization and SLAM. Unlike traditional methods requiring expensive per-pixel depth data, Depth2Pose utilizes readily available camera poses, enabling evaluation in challenging environments where ground-truth depth is difficult to acquire. The accompanying D2P dataset features scenes outside the typical distribution of existing training data, highlighting potential generalization issues with current models. AI
IMPACT Introduces a new evaluation framework for depth estimation models, potentially improving their utility in real-world geometric applications.