[2602.23574] Evidential Neural Radiance Fields

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.23574 (cs) [Submitted on 27 Feb 2026] Title:Evidential Neural Radiance Fields Authors:Ruxiao Duan, Alex Wong View a PDF of the paper titled Evidential Neural Radiance Fields, by Ruxiao Duan and Alex Wong View PDF HTML (experimental) Abstract:Understanding sources of uncertainty is fundamental to trustworthy three-dimensional scene modeling. While recent advances in neural radiance fields (NeRFs) achieve impressive accuracy in scene reconstruction and novel view synthesis, the lack of uncertainty estimation significantly limits their deployment in safety-critical settings. Existing uncertainty quantification methods for NeRFs fail to capture both aleatoric and epistemic uncertainty. Among those that do quantify one or the other, many of them either compromise rendering quality or incur significant computational overhead to obtain uncertainty estimates. To address these issues, we introduce Evidential Neural Radiance Fields, a probabilistic approach that seamlessly integrates with the NeRF rendering process and enables direct quantification of both aleatoric and epistemic uncertainty from a single forward pass. We compare multiple uncertainty quantification methods on three standardized benchmarks, where our approach demonstrates state-of-the-art scene reconstruction fidelity and uncertainty estimation quality. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Mach...