Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance
for upcoming applications in AR or VR. These range from mixed reality applications for …

With the introduction of Neural Radiance Fields (NeRFs), novel view synthesis has recently
made a big leap forward. At the core, NeRF proposes that each 3D point can emit radiance …

Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing
surfaces from multi-view images and synthesizing novel views. Unfortunately, existing …

Abstract We present Depth-aware Image-based NEural Radiance fields (DINER). Given a
sparse set of RGB input views, we predict depth and feature maps to guide the …

Abstract Neural Radiance Fields (NeRFs) are a very recent and very popular approach for
the problems of novel view synthesis and 3D reconstruction. A popular scene representation …

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged
as a compelling technique for learning to represent 3D scenes from images with the goal of …

We present a super-fast convergence approach to reconstructing the per-scene radiance
field from a set of images that capture the scene with known poses. This task, which is often …

Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual
quality. However, to render photorealistic images, NeRFs require hundreds of deep …

Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of
capture settings, including 360 capture of bounded scenes and forward-facing capture of …

Although neural radiance fields (NeRF) have shown impressive advances in novel view
synthesis, most methods require multiple input images of the same scene with accurate …