Local Implicit Ray Function for Generalizable Radiance Field Representation
We propose LIRF (Local Implicit Ray Function), a generalizable neural rendering approach for novel view rendering. Current generalizable neural radiance fields (NeRF) methods sample a scene with a single ray per pixel and may therefore render blurred or aliased views when the input views and rendered views capture scene content with different resolutions. To solve this problem, we propose LIRF to aggregate the information from conical frustums to construct a ray. Given 3D positions within conical frustums, LIRF takes 3D coordinates and the features of conical frustums as inputs and predicts a local volumetric radiance field. Since the coordinates are continuous, LIRF renders high-quality novel views at a continuously-valued scale via volume rendering. Besides, we predict the visible weights for each input view via transformer-based feature matching to improve the performance in occluded areas. Experimental results on real-world scenes validate that our method outperforms state-of-the-art methods on novel view rendering of unseen scenes at arbitrary scales.
Code (0)
등록된 구현이 없습니다.
Tasks
NeRFNeural RenderingSimilar Papers 제목 키워드 기반
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field(NeRF) is chosen as the 3D scene represe…
NeRFNeural RenderingVisual LocalizationGeneralizable NGP-SR: Generalizable Neural Radiance Fields Super-Resolution via Neural Graph Primitives
Neural Radiance Fields (NeRF) achieve photorealistic novel view synthesis but become costly when high-resolution (HR) rendering is required, as HR outputs demand dense sampling and higher-capacity models. Moreover, naive…
Novel View SynthesisLearning Generalizable Light Field Networks from Few Images
We explore a new strategy for few-shot novel view synthesis based on a neural light field representation. Given a target camera pose, an implicit neural network maps each ray to its target pixel's color directly. The net…
Novel View SynthesisCascaded and Generalizable Neural Radiance Fields for Fast View Synthesis
We present CG-NeRF, a cascade and generalizable neural radiance fields method for view synthesis. Recent generalizing view synthesis methods can render high-quality novel views using a set of nearby input views. However,…
GPUNeRFNeural RenderingNovel View SynthesisVolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize pe…
NeRFNovel View Synthesis