The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel
view synthesis. Unlike neural implicit representations like neural radiance fields (NeRFs) …

3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to
become a mainstream method for 3D representations. It can effectively transform multi-view …

In this study, we explore the challenge of efficiently representing scenes with a constrained
number of Gaussians. Our analysis shifts from traditional graphics and 2D computer vision to …

Implicit neural representations (INRs) recently achieved great success in image
representation and compression, offering high visual quality and fast rendering speeds with …

In this paper we present EgoLifter, a novel system that can automatically segment scenes
captured from egocentric sensors into a complete decomposition of individual 3D objects …

Large garages are ubiquitous yet intricate scenes that present unique challenges due to
their monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle …

3D Gaussian splatting (3D GS) has recently emerged as a transformative technique in the
explicit radiance field and computer graphics landscape. This innovative approach …

Abstract Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D
Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data …

Closed-loop simulation is essential for advancing end-to-end autonomous driving systems.
Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on …

We present SCube, a novel method for reconstructing large-scale 3D scenes (geometry,
appearance, and semantics) from a sparse set of posed images. Our method encodes …