4 citations · 10 across the 17 of their papers we have counts for
22 papers
SuperFlex: Deformable Superquadrics for Point Cloud Decomposition
Gabriel Tavernini, Elisabetta Fedele, Tiago Novello +3
Superquadrics have proven to provide a compact, geometrically meaningful representation for 3D objects. However, existing methods suffer from limited reconstruction accuracy, are r…
Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces
Xinggang Hu, Chenyangguang Zhang, Alexandros Delitzas +4
Functional 3D scene graphs offer a versatile and flexible representation for 3D scene understanding and robotic manipulation, defined by object nodes, interactive elements, and fun…
FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos
Alexandros Delitzas, Chenyangguang Zhang, Alexey Gavryushin +8
We present FunRec, a method for reconstructing functional 3D digital twins of indoor scenes directly from egocentric RGB-D interaction videos. Unlike existing methods on articulate…
HouseLayout3D: A Benchmark and Training-Free Baseline for 3D Layout Estimation in the Wild
Valentin Bieri, Marie-Julie Rakotosaona, Keisuke Tateno +2
Current 3D layout estimation models are primarily trained on synthetic datasets containing simple single room or single floor environments. As a consequence, they cannot natively h…
SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling
Elisabetta Fedele, Francis Engelmann, Ian Huang +3
Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing…
UnLoc: Leveraging Depth Uncertainties for Floorplan Localization
Matthias Wüest, Francis Engelmann, Ondrej Miksik +2
We propose UnLoc, an efficient data-driven solution for sequential camera localization within floorplans. Floorplan data is readily available, long-term persistent, and robust to c…
Video Perception Models for 3D Scene Synthesis
Rui Huang, Guangyao Zhai, Zuria Bauer +5
Traditionally, 3D scene synthesis requires expert knowledge and significant manual effort. Automating this process could greatly benefit fields such as architectural design, roboti…
Robust Human Registration with Body Part Segmentation on Noisy Point Clouds
Kai Lascheit, Daniel Barath, Marc Pollefeys +2
Registering human meshes to 3D point clouds is essential for applications such as augmented reality and human-robot interaction but often yields imprecise results due to noise and…
SuperDec: 3D Scene Decomposition with Superquadric Primitives
Elisabetta Fedele, Boyang Sun, Leonidas Guibas +2
We present SuperDec, an approach for creating compact 3D scene representations via decomposition into superquadric primitives. While most recent works leverage geometric primitives…
Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces
Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang +4
We introduce the task of predicting functional 3D scene graphs for real-world indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs that focus on spatial…
ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
Guangda Ji, Silvan Weder, Francis Engelmann +2
Neural network performance scales with both model size and data volume, as shown in both language and image processing. This requires scaling-friendly architectures and large datas…
Search3D: Hierarchical Open-Vocabulary 3D Segmentation
Ayca Takmaz, Alexandros Delitzas, Robert W. Sumner +3
Open-vocabulary 3D segmentation enables exploration of 3D spaces using free-form text descriptions. Existing methods for open-vocabulary 3D instance segmentation primarily focus on…
P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising
Mathias Vogel, Keisuke Tateno, Marc Pollefeys +3
In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schrödinger bridges to points clouds. Unlike previous approaches that pred…
OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation
Gonca Yilmaz, Songyou Peng, Marc Pollefeys +2
Recently, Vision-Language Models (VLMs) have advanced segmentation techniques by shifting from the traditional segmentation of a closed-set of predefined object classes to open-voc…
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds
Oliver Lemke, Zuria Bauer, René Zurbrügg +3
In recent years, modern techniques in deep learning and large-scale datasets have led to impressive progress in 3D instance segmentation, grasp pose estimation, and robotics. This…
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Francis Engelmann, Fabian Manhardt, Michael Niemeyer +3
Large visual-language models (VLMs), like CLIP, enable open-set image segmentation to segment arbitrary concepts from an image in a zero-shot manner. This goes beyond the tradition…
SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs
Yang Miao, Francis Engelmann, Olga Vysotska +3
We introduce a novel problem, i.e., the localization of an input image within a multi-modal reference map represented by a database of 3D scene graphs. These graphs comprise multip…
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
Francis Engelmann, Ayca Takmaz, Jonas Schult +25
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023. The goal of this wo…
Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels
Rui Huang, Songyou Peng, Ayca Takmaz +5
Current 3D scene segmentation methods are heavily dependent on manually annotated 3D training datasets. Such manual annotations are labor-intensive, and often lack fine-grained det…
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction
Silvan Weder, Francis Engelmann, Johannes L. Schönberger +3
We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly appli…
LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
Silvan Weder, Hermann Blum, Francis Engelmann +1
Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire. This work introduces a fully automated 2D/3D labeling framework that, wit…
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes
Julian Chibane, Francis Engelmann, Tuan Anh Tran +1
Current 3D segmentation methods heavily rely on large-scale point-cloud datasets, which are notoriously laborious to annotate. Few attempts have been made to circumvent the need fo…