Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction,
achieving high quality novel view synthesis and fast rendering speed. However, 3DGS fails …

Neural rendering methods have significantly advanced photo-realistic 3D scene rendering
in various academic and industrial applications. The recent 3D Gaussian Splatting method …

Traditional approaches for learning 3D object categories have been predominantly trained
and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Abstract 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis
results and advancing real-time rendering performance. However, the effectiveness of 3DGS …

Deep learning has significantly improved 2D image recognition. Extending into 3D may
advance many new applications including autonomous vehicles, virtual and augmented …

In this paper we present ADOP, a novel point-based, differentiable neural rendering
pipeline. Like other neural renderers, our system takes as input calibrated camera images …

Real-time rendering and animation of humans is a core function in games, movies, and
telepresence applications. Existing methods have a number of drawbacks we aim to …