解决三平面辐射场在相机位姿噪声下的失真问题，提升重建鲁棒性。
Disentangled Generation and Aggregation for Robust Radiance Fields
- 分离生成与聚合：引入全局上下文和光滑性约束，缓解局部更新误差
- 在噪声/未知位姿下实现最优新视角合成效果，收敛更快
- 适合需要高鲁棒性3D重建的场景，如真实拍摄数据
近年来，基于三平面的辐射场因其能以高质量表示和低计算成本有效解耦三维场景而受到关注。该方法的关键要求是精确的相机位姿输入。然而，由于三平面具有局部更新特性，类似以往联合姿态-NeRF优化的方法容易陷入局部最小值。为此，我们提出解耦三平面生成模块，在三平面学习中引入全局特征上下文和光滑性，缓解局部更新带来的误差；同时提出解耦平面聚合机制，减轻相机位姿更新过程中因共用三平面特征聚合导致的纠缠问题。此外，我们引入两阶段热启动训练策略，降低三平面生成器带来的隐式约束。定量与定性结果表明，所提方法在相机位姿存在噪声或未知的情况下，均实现了当前最佳的新视角合成性能，并具备高效优化收敛能力。
原文摘要 · Abstract (English)
The utilization of the triplane-based radiance fields has gained attention in recent years due to its ability to effectively disentangle 3D scenes with a high-quality representation and low computation cost. A key requirement of this method is the precise input of camera poses. However, due to the local update property of the triplane, a similar joint estimation as previous joint pose-NeRF optimization works easily results in local minima. To this end, we propose the Disentangled Triplane Generation module to introduce global feature context and smoothness into triplane learning, which mitigates errors caused by local updating. Then, we propose the Disentangled Plane Aggregation to mitigate the entanglement caused by the common triplane feature aggregation during camera pose updating. In addition, we introduce a two-stage warm-start training strategy to reduce the implicit constraints caused by the triplane generator. Quantitative and qualitative results demonstrate that our proposed method achieves state-of-the-art performance in novel view synthesis with noisy or unknown camera poses, as well as efficient convergence of optimization. Project page: https://gaohchen.github.io/DiGARR/.
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