# 3DTV：面向实时视角合成的前馈插值网络

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-13 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnzxqigf016lslwzzl6hf0yp
- 原文链接：https://arxiv.org/abs/2604.11211

## AI 摘要

研究团队推出3DTV前馈网络，实现实时稀疏视角插值与视图合成。该方法结合轻量级几何与深度学习，通过Delaunay三元组选择确保角度覆盖，并引入姿态感知深度模块估计多尺度深度金字塔，支持高效特征重投影与遮挡感知混合。与需要场景特定优化的方法不同，3DTV无需再训练即可前馈运行，在挑战性多视角视频数据集上质量与效率均衡，性能优于现有实时基线，适用于AR/VR、远程呈现等低延迟交互场景。

## 正文

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/