Pano3D: A Holistic Benchmark and a Solid Baseline for $360^o$ Depth Estimation
This provides a comprehensive evaluation framework for researchers in computer vision working on panoramic depth estimation, though it is incremental as it builds on existing depth estimation methods.
The authors introduced Pano3D, a benchmark for 360-degree depth estimation that evaluates performance across multiple traits like precision, accuracy, boundary preservation, and smoothness, and includes inter-dataset assessment to measure generalization, resulting in a baseline for future research.
Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits, boundary preservation, and smoothness. Moreover, Pano3D moves beyond typical intra-dataset evaluation to inter-dataset performance assessment. By disentangling the capacity to generalize to unseen data into different test splits, Pano3D represents a holistic benchmark for $360^o$ depth estimation. We use it as a basis for an extended analysis seeking to offer insights into classical choices for depth estimation. This results in a solid baseline for panoramic depth that follow-up works can build upon to steer future progress.