PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation

TL;DR

This paper introduces PanoVOS, a new panoramic video dataset, and proposes PSCFormer, a transformer-based model that effectively handles pixel-level content discontinuities for improved panoramic video segmentation.

Contribution

The paper provides the first panoramic video dataset for segmentation and introduces PSCFormer, a transformer model that leverages semantic boundary information for better performance.

Findings

Existing models fail on panoramic videos due to content discontinuities.

PSCFormer outperforms previous state-of-the-art models in panoramic video segmentation.

PanoVOS dataset presents new challenges for panoramic video analysis.

Abstract

Panoramic videos contain richer spatial information and have attracted tremendous amounts of attention due to their exceptional experience in some fields such as autonomous driving and virtual reality. However, existing datasets for video segmentation only focus on conventional planar images. To address the challenge, in this paper, we present a panoramic video dataset, PanoVOS. The dataset provides 150 videos with high video resolutions and diverse motions. To quantify the domain gap between 2D planar videos and panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS) models on PanoVOS. Through error analysis, we found that all of them fail to tackle pixel-level content discontinues of panoramic videos. Thus, we present a Panoramic Space Consistency Transformer (PSCFormer), which can effectively utilize the semantic boundary information of the previous frame for pixel-level matching with the current frame. Extensive experiments demonstrate that compared with the previous SOTA models, our PSCFormer network exhibits a great advantage in terms of segmentation results under the panoramic setting. Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking.