Point-based rendering techniques

TL;DR: 3D Gaussian splatting achieves high-quality novel-view synthesis with real-time rendering at 1080p resolution.

TL;DR: This work proposes a novel differentiable point cloud renderer that is used to transform a latent 3D point cloud of features into the target view and outperforms baselines and prior work on the Matterport, Replica, and RealEstate10K datasets.

TL;DR: Loss functions for Neural Rendering Jun-Yan Zhu shows the importance of knowing the number of neurons in the system and how many neurons are firing at the same time.

TL;DR: In this article , a state-of-the-art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often referred to as neural scene representations.

TL;DR: A novel method called visibility splatting determines visible surfels and holes in the z-buffer, which makes them specifically suited for low-cost, real-time graphics, such as games.

TL;DR: This work has implemented incremental and hierarchical clustering, iterative simplification, and particle simulation algorithms to create approximations of point-based models with lower sampling density, and shows how local variation estimation and quadric error metrics can be employed to diminish the approximation error.

TL;DR: A point rendering and texture filtering technique called surface splatting which directly renders opaque and transparent surfaces from point clouds without connectivity based on a novel screen space formulation of the Elliptical Weighted Average (EWA) filter is described.

TL;DR: A method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise is described and analyzed.