Search Results for author:
Found 13 papers, 4 papers with code
P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising
In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr\"odinger bridges to points clouds.
Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians
In response, we propose the first RGB-only SLAM system with a dense 3D Gaussian map representation that utilizes all benefits of globally optimized tracking by adapting dynamically to keyframe pose and depth updates by actively deforming the 3D Gaussian map.
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Our OpenNeRF further leverages NeRF's ability to render novel views and extract open-set VLM features from areas that are not well observed in the initial posed images.
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS
First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization.
Incremental 3D Semantic Scene Graph Prediction from RGB Sequences
Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network.
NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM
However, in real-time and on-the-fly scene capture applications, this prior knowledge cannot be assumed as fixed or static, since it dynamically changes and it is subject to significant updates based on run-time observations.
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks.
Ranked #1 on
3D Object Classification
on 3R-Scan
A Divide et Impera Approach for 3D Shape Reconstruction from Multiple Views
Estimating the 3D shape of an object from a single or multiple images has gained popularity thanks to the recent breakthroughs powered by deep learning.
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
We propose a framework that ameliorates this issue by performing scene reconstruction and semantic scene completion jointly in an incremental and real-time manner, based on an input sequence of depth maps.
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images
There is a high demand of 3D data for 360° panoramic images and videos, pushed by the growing availability on the market of specialized hardware for both capturing (e. g., omnidirectional cameras) as well as visualizing in 3D (e. g., head mounted displays) panoramic images and videos.
Ranked #10 on
Depth Estimation
on Stanford2D3D Panoramic
Peeking Behind Objects: Layered Depth Prediction from a Single Image
While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects.
Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation
We propose an efficient and scalable method for incrementally building a dense, semantically annotated 3D map in real-time.
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction.
