Search Results for author:
Found 17 papers, 8 papers with code
LoopSplat: Loop Closure by Registering 3D Gaussian Splats
However, existing 3DGS-based methods fail to address the global consistency of the scene via loop closure and/or global bundle adjustment.
Point Cloud Registration
Simultaneous Localization and Mapping
"Where am I?" Scene Retrieval with Language
We define this task as "language-based scene-retrieval" and it is closely related to "coarse-localization," but we are instead searching for a match from a collection of disjoint scenes and not necessarily a large-scale continuous map.
I-Design: Personalized LLM Interior Designer
Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality.
Multiway Point Cloud Mosaicking with Diffusion and Global Optimization
We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) designed to co-align sets of partially overlapping point clouds -- typically obtained from 3D scanners or moving RGB-D cameras -- into a unified coordinate system.
Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
Research into dynamic 3D scene understanding has primarily focused on short-term change tracking from dense observations, while little attention has been paid to long-term changes with sparse observations.
Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change
To this end, we introduce the Nothing Stands Still (NSS) benchmark, which focuses on the spatiotemporal registration of 3D scenes undergoing large spatial and temporal change, ultimately creating one coherent spatiotemporal map.
Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature
Point cloud registration has seen recent success with several learning-based methods that focus on correspondence matching and, as such, optimize only for this objective.
Volumetric Semantically Consistent 3D Panoptic Mapping
We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating comprehensive, accurate, and efficient semantic 3D maps suitable for autonomous agents in unstructured environments.
Learning-based Relational Object Matching Across Views
Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.
SGAligner : 3D Scene Alignment with Scene Graphs
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (ie, unknown overlap -- if any -- and changes in the environment).
Ranked #2 on
Point Cloud Registration
on 3RScan
SGAligner: 3D Scene Alignment with Scene Graphs
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (i. e., unknown overlap - if any - and changes in the environment).
ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields
To make full use of the point cloud and the underlying images, we introduce ImpliCity, a neural representation of the 3D scene as an implicit, continuous occupancy field, driven by learned embeddings of the point cloud and a stereo pair of ortho-photos.
Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation
Vision-based robotics often separates the control loop into one module for perception and a separate module for control.
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e. g., class, material, and other attributes), rooms (e. g., scene category, volume, etc.)
SEGCloud: Semantic Segmentation of 3D Point Clouds
Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation.
Ranked #13 on
Semantic Segmentation
on Semantic3D
Joint 2D-3D-Semantic Data for Indoor Scene Understanding
We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2. 5D and 3D domains, with instance-level semantic and geometric annotations.
3D Semantic Parsing of Large-Scale Indoor Spaces
In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e. g. rooms, etc) that are aligned into a canonical reference coordinate system.
