We introduce RANRAC, a robust reconstruction algorithm for 3D objects handling occluded and distracted images, which is a particularly challenging scenario that prior robust reconstruction methods cannot deal with. Our solution supports single-shot reconstruction by involving light-field networks, and is also applicable to photo-realistic, robust, multi-view reconstruction from real-world images based on neural radiance fields. While the algorithm imposes certain limitations on the scene representation and, thereby, the supported scene types, it reliably detects and excludes inconsistent perspectives, resulting in clean images without floating artifacts. Our solution is based on a fuzzy adaption of the random sample consensus paradigm, enabling its application to large scale models. We interpret the minimal number of samples to determine the model parameters as a tunable hyperparameter. This is applicable, as a cleaner set of samples improves reconstruction quality. Further, this procedure also handles outliers. Especially for conditioned models, it can result in the same local minimum in the latent space as would be obtained with a completely clean set. We report significant improvements for novel-view synthesis in occluded scenarios, of up to 8dB PSNR compared to the baseline.

翻译：我们提出RANRAC，一种处理遮挡和干扰图像的三维物体鲁棒重建算法，这是此前鲁棒重建方法难以应对的极具挑战性场景。本方案通过引入光场网络支持单次重建，并适用于基于神经辐射场的真实世界图像的光照真实感鲁棒多视角重建。虽然该算法对场景表示形式及支持的场景类型施加了特定限制，但能够可靠检测并排除不一致视角，从而生成无漂浮伪影的清晰图像。本方案基于随机采样共识范式的模糊适配实现，使其能够应用于大规模模型。我们将确定模型参数所需的最小样本数视为可调超参数——由于更纯净的样本集能提升重建质量，该设定具有切实可行性。此外，该流程同样能处理异常值。对于条件化模型，该方法在潜空间中收敛至的局部最小值，可与完全纯净样本集获得的结果保持一致。实验表明，在遮挡场景的新视角合成任务中，我们的方法相较基线实现了高达8dB PSNR的显著提升。