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 …

Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by
encoding neural representations of a scene. However, NeRFs require hundreds of images …

The angular information of light lost in conventional images but preserved and stored in light-
fields plays an instrumental role in many applications such as depth estimation, 3D …

Recent generalizable NeRF methods synthesize novel view images without optimizing per-
scene via constructing radiation fields from 2D features. However, most of the existing …

In this paper, we present an efficient and robust deep learning solution for novel view
synthesis of complex scenes. In our approach, a 3D scene is represented as a light field, ie …

We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis
and relighting using multi-view image inputs. Existing neural rendering (NR) does not …

Although many recent works have investigated generalizable NeRF-based novel view
synthesis for unseen scenes, they seldom consider the synthetic-to-real generalization …

The emergence of the generalized neural radiance field models has greatly expanded the
applicability of novel view synthesis task. However, existing methods primarily rely on 2D …

With the emergence of neural radiance fields (NeRFs), view synthesis quality has reached
an unprecedented level. Compared to traditional mesh-based assets, this volumetric …

Novel view synthesis from a single image requires inferring occluded regions of objects and
scenes whilst simultaneously maintaining semantic and physical consistency with the input …