text: 93,130,1402907147181101059,841031248839618560,Relja Arandjelović,"In our new paper ""NeRF in detail: Learning to sample for view synthesis"" (aka yet another NeRF paper on your to-read list) we replace the heuristic coarse-to-fine strategy of NeRF via a learnt one. Improvements in rendering quality and speed. <LINK> <LINK> @SattlerTorsten Thanks Torsten! I only tried it really on the original NeRF paper's datasets, it seems most NeRF improvements use the same data as well. @SattlerTorsten I see, good to know.",https://arxiv.org/abs/2106.05264,"Neural radiance fields (NeRF) methods have demonstrated impressive novel view synthesis performance. The core approach is to render individual rays by querying a neural network at points sampled along the ray to obtain the density and colour of the sampled points, and integrating this information using the rendering equation. Since dense sampling is computationally prohibitive, a common solution is to perform coarse-to-fine sampling. In this work we address a clear limitation of the vanilla coarse-to-fine approach -- that it is based on a heuristic and not trained end-to-end for the task at hand. We introduce a differentiable module that learns to propose samples and their importance for the fine network, and consider and compare multiple alternatives for its neural architecture. Training the proposal module from scratch can be unstable due to lack of supervision, so an effective pre-training strategy is also put forward. The approach, named `NeRF in detail' (NeRF-ID), achieves superior view synthesis quality over NeRF and the state-of-the-art on the synthetic Blender benchmark and on par or better performance on the real LLFF-NeRF scenes. Furthermore, by leveraging the predicted sample importance, a 25% saving in computation can be achieved without significantly sacrificing the rendering quality. ",NeRF in detail: Learning to sample for view synthesis,3,"['In our new paper ""NeRF in detail: Learning to sample for view synthesis"" (aka yet another NeRF paper on your to-read list) we replace the heuristic coarse-to-fine strategy of NeRF via a learnt one. Improvements in rendering quality and speed. <LINK> <LINK>', ""@SattlerTorsten Thanks Torsten! I only tried it really on the original NeRF paper's datasets, it seems most NeRF improvements use the same data as well."", '@SattlerTorsten I see, good to know.']",21,06,446