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Found 17 papers, 3 papers with code
Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians
The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations.
GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction
We show on diverse scenes that our design unlocks the potential for more accurate and detailed surface reconstructions, and at the meantime benefits 3DGS rendering with structures that are more aligned with the underlying geometry.
Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications.
VR-NeRF: High-Fidelity Virtualized Walkable Spaces
We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields.
MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond
While most of recent neural rendering works focus on objects and small-scale scenes, developing neural rendering methods for city-scale scenes is of great potential in many real-world applications.
Grid-guided Neural Radiance Fields for Large Urban Scenes
An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene with increased grid resolutions.
AssetField: Assets Mining and Reconfiguration in Ground Feature Plane Representation
Traditional modeling pipelines keep an asset library storing unique object templates, which is both versatile and memory efficient in practice.
Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production
Amateurs working on mini-films and short-form videos usually spend lots of time and effort on the multi-round complicated process of setting and adjusting scenes, plots, and cameras to deliver satisfying video shots.
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images
More precisely, the OmniCity contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations in New York City.
BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering
The wide span of viewing positions within these scenes yields multi-scale renderings with very different levels of detail, which poses great challenges to neural radiance field and biases it towards compromised results.
BlockPlanner: City Block Generation With Vectorized Graph Representation
City modeling is the foundation for computational urban planning, navigation, and entertainment.
Demystify Painting with RL
Given an image of an arbitrary scene, experienced artists are skillful at accurately perceiving the visual contents within the scene, such as objects, lighting, and tint, and presenting them in different painting styles.
A Unified Framework for Shot Type Classification Based on Subject Centric Lens
Shots are key narrative elements of various videos, e. g. movies, TV series, and user-generated videos that are thriving over the Internet.
Online Multi-modal Person Search in Videos
The task of searching certain people in videos has seen increasing potential in real-world applications, such as video organization and editing.
Learn to Propagate Reliably on Noisy Affinity Graphs
Recent works have shown that exploiting unlabeled data through label propagation can substantially reduce the labeling cost, which has been a critical issue in developing visual recognition models.
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
Scene, as the crucial unit of storytelling in movies, contains complex activities of actors and their interactions in a physical environment.
A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM
Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays.
