LFX: ライトフィールドの密な意味セグメンテーションと顕著物体検出を統合するフレームワーク
LFX: Towards Unified Light Field Dense Semantic Segmentation and Salient Object Detection
ライトフィールドの異なる表現形式に依存せず、意味セグメンテーションと顕著物体検出を同時に扱える初の統一学習フレームワークLFXを提案し、複数ベンチマークで最高精度を達成した。
著者: Fei Teng, Lingxin Huang, Buyin Deng, Kai Luo, Boyuan Zheng, Zheng Fang, Hong Zheng, Kunyu Peng, Jiaming Zhang, Yaonan Wang, Kailun Yang
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
Light field cameras capture multi-view observations within a single exposure. However, existing studies are typically tailored to specific LF representations, leaving the field without a unified learning framework. To bridge this gap, we present LFX, the first unified framework for LF perception. LFX establishes a representation-invariant feature modulation space, enabling it to adapt to heterogeneous LF representations and diverse perception tasks. Specifically, we propose Field-of-Parallax Angular Subspace Modeling (FoP-ASM), which assigns an independent angular marker to each auxiliary view, enabling view-wise independent modeling. Meanwhile, shared manifold subspace constraints and regularization losses enforce globally consistent semantic modulation across views. Extensive evaluations across three LF benchmarks show that LFX achieves state-of-the-art results across distinct LF representations, outperforming representation-specific methods by up to 12% and 20% with 0.029/0.027 MAE for salient object detection, and achieving 84.37 mIoU for semantic segmentation. The source code will be made publicly available at https://github.com/FeiT-FeiTeng/LFX.
