3DTV实现实时稀疏视角插值，无需重训练即可高质量渲染。
3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis
- 基于Delaunay三角剖分选择视角，保证目标视图的视角覆盖。
- 提出姿态感知深度模块，分层估计深度图并高效重投影特征。
- 无需场景特定优化，适合AR/VR、远程会议等低延迟应用。
实时自由视角渲染需在多相机冗余与交互应用的延迟约束间取得平衡。本文提出3DTV，一种用于实时稀疏视角插值的前馈网络。通过Delaunay三角剖分选择视角对，确保每个目标视图的角覆盖。在此基础上，引入姿态感知深度模块，生成粗到细的深度金字塔，实现高效特征重投影与遮挡感知融合。与依赖场景特定优化的方法不同，3DTV无需重新训练即可前馈运行，适用于AR/VR、远程呈现和交互应用。在挑战性多视角视频数据集上的实验表明，3DTV在质量和效率之间保持良好平衡，优于近期实时新视角生成基线方法。关键优势在于避免显式代理，可在多样场景中实现鲁棒渲染，是低延迟多视角流媒体和交互渲染的实用方案。
原文摘要 · Abstract (English)
Real-time free-viewpoint rendering requires balancing multi-camera redundancy with the latency constraints of interactive applications. We address this challenge by combining lightweight geometry with learning and propose 3DTV, a feedforward network for real-time sparse-view interpolation. A Delaunay-based triplet selection ensures angular coverage for each target view. Building on this, we introduce a pose-aware depth module that estimates a coarse-to-fine depth pyramid, enabling efficient feature reprojection and occlusion-aware blending. Unlike methods that require scene-specific optimization, 3DTV runs feedforward without retraining, making it practical for AR/VR, telepresence, and interactive applications. Our experiments on challenging multi-view video datasets demonstrate that 3DTV consistently achieves a strong balance of quality and efficiency, outperforming recent real-time novel-view baselines. Crucially, 3DTV avoids explicit proxies, enabling robust rendering across diverse scenes. This makes it a practical solution for low-latency multi-view streaming and interactive rendering. Project Page: https://stefanmschulz.github.io/3DTV_webpage/
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