Search Results for author:
Found 11 papers, 2 papers with code
GASPACHO: Gaussian Splatting for Controllable Humans and Objects
Several experiments on two human-object datasets - BEHAVE and DNA-Rendering - demonstrate that our method allows for high-quality reconstruction of human and object templates under significant occlusion and the synthesis of controllable renderings of novel human-object interactions in novel human poses from novel camera views.
Better Together: Unified Motion Capture and 3D Avatar Reconstruction
We present Better Together, a method that simultaneously solves the human pose estimation problem while reconstructing a photorealistic 3D human avatar from multi-view videos.
SCRREAM : SCan, Register, REnder And Map:A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark
However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details.
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
We introduce 3DGS-Calib, a new calibration method that relies on the speed and rendering accuracy of 3D Gaussian Splatting to achieve multimodal spatiotemporal calibration that is accurate, robust, and with a substantial speed-up compared to methods relying on implicit neural representations.
SWinGS: Sliding Windows for Dynamic 3D Gaussian Splatting
Training a separate dynamic 3D Gaussian model for each sliding window allows the canonical representation to change, enabling the reconstruction of scenes with significant geometric changes.
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields.
Human Gaussian Splatting: Real-time Rendering of Animatable Avatars
This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos.
CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representation
Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world.
ImPosing: Implicit Pose Encoding for Efficient Visual Localization
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments.
LENS: Localization enhanced by NeRF synthesis
Neural Radiance Fields (NeRF) have recently demonstrated photo-realistic results for the task of novel view synthesis.
CoordiNet: uncertainty-aware pose regressor for reliable vehicle localization
In this setup, structure-based methods require a large database, and we show that our proposal is a reliable alternative, achieving 29cm median error in a 1. 9km loop in a busy urban area
Ranked #2 on
Camera Localization
on Oxford RobotCar Full
