Search Results for author:
Found 13 papers, 4 papers with code
HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes
The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings.
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization
We propose an optimal viewpoint selection strategy, that finds the most miniature set of viewpoints covering all the faces of a mesh.
GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time
Specifically, we design a novel joint learning framework that consists of an Iterative Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model.
GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos
In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA).
GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
This paper presents GIR, a 3D Gaussian Inverse Rendering method for relightable scene factorization.
DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes
Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand.
Understanding In-Context Learning from Repetitions
This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs).
Digging into Depth Priors for Outdoor Neural Radiance Fields
However, the shape-radiance ambiguity of radiance fields remains a challenge, especially in the sparse viewpoints setting.
MapNeRF: Incorporating Map Priors into Neural Radiance Fields for Driving View Simulation
The key insight is that map information can be utilized as a prior to guiding the training of the radiance fields with uncertainty.
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
This paper presents a new method that uses scores provided by humans instead of pairwise preferences to improve the feedback efficiency of interactive reinforcement learning.
NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering
This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces using pre-integrated rendering from multi-view images or video.
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
This setting results in two distinct domain gaps: scenarios and sensors, making it difficult to analyze and evaluate the method accurately.
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space
Current evaluation of an NMT system is usually built upon a heuristic decoding algorithm (e. g., beam search) and an evaluation metric assessing similarity between the translation and golden reference.
