Pano3D: A Holistic Benchmark and a Solid Baseline for $360^o$ Depth Estimation

Abstract

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 in 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 into a solid baseline for panoramic depth that followup works can built upon to steer future progress.

Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images

“…Then, we present a visual comparison among the three methods at the same rate, which is evaluated by the bit budget. Figure 8 shows the test images from the Pano3D dataset [34]. All the four test images are converted to 8-bit greyscale images.…”

“…Several experiments are conducted to assess the compression performance of the proposed method, categorized into three parts. In the initial phase, we present the distribution of DCT coefficients produced by images from the Pano3D dataset [34] and explain our rationale for using empirical distributions. In the second phase, we apply our methodologies to the JPEG quantization table.…”

“…Instead, we perform a comparative analysis of our methods against two previously JPEG-based approaches, utilizing the LIC360 dataset [5]. In the last phase, our methods are juxtaposed with two novel integer bit allocation approaches, utilizing both the Pano3D dataset [34] and LIC360 dataset [5].…”

“…In this section, we display the statistic coefficients of 100 images sourced from the Pano3D dataset [34]. Our analysis adopts a block length configuration of L = 8, resulting in a total of 64 coefficients within each block, derived from the 8 × 8 DCT matrix.…”