69 citations · 186 across the 18 of their papers we have counts for
20 papers
AugSplat: Radiance Field-Informed Gaussian Splatting for Sparse-View Settings
Lorenzo Lazzaroni, Riccardo Bollati, Daniel Barath +2
Generating high-quality novel views at real-time frame rates remains a central challenge in 3D vision, particularly in sparse-view scenarios. Neural radiance fields have demonstrat…
DynaTok: Token-Based 4D Reconstruction from Partial Point Clouds
Weirong Chen, Keisuke Tateno, Hidenobu Matsuki +3
We address 4D reconstruction from partial point cloud sequences, where depth-sensor observations are incomplete, unordered, and lack explicit temporal correspondences. This geometr…
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…
DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management
Casimir Feldmann, Maximum Wilder-Smith, Vaishakh Patil +4
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated impressive results for novel view synthesis with real-time rendering capabilities. However, integrating 3DGS with…
Learning Neural Exposure Fields for View Synthesis
Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona +5
Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchma…
RiemanLine: Riemannian Manifold Representation of 3D Lines for Factor Graph Optimization
Yan Li, Ze Yang, Keisuke Tateno +3
Minimal parametrization of 3D lines plays a critical role in camera localization and structural mapping. Existing representations in robotics and computer vision predominantly hand…
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…
Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians
Erik Sandström, Keisuke Tateno, Michael Oechsle +4
3D Gaussian Splatting has emerged as a powerful representation of geometry and appearance for RGB-only dense Simultaneous Localization and Mapping (SLAM), as it provides a compact…
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…
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS
Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona +7
Recent advances in view synthesis and real-time rendering have achieved photorealistic quality at impressive rendering speeds. While Radiance Field-based methods achieve state-of-t…
Incremental 3D Semantic Scene Graph Prediction from RGB Sequences
Shun-Cheng Wu, Keisuke Tateno, Nassir Navab +1
3D semantic scene graphs are a powerful holistic representation as they describe the individual objects and depict the relation between them. They are compact high-level graphs tha…
NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM
Hidenobu Matsuki, Keisuke Tateno, Michael Niemeyer +1
Neural field-based 3D representations have recently been adopted in many areas including SLAM systems. Current neural SLAM or online mapping systems lead to impressive results in t…
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Takuma Udagawa, Haruka Kiyohara, Yusuke Narita +2
Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, the…
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service
Yuta Saito, Takuma Udagawa, Kei Tateno
Off-policy evaluation (OPE) is the method that attempts to estimate the performance of decision making policies using historical data generated by different policies without conduc…
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Shun-Cheng Wu, Johanna Wald, Keisuke Tateno +2
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semanti…
A Divide et Impera Approach for 3D Shape Reconstruction from Multiple Views
Riccardo Spezialetti, David Joseph Tan, Alessio Tonioni +2
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. Most approaches regress the…
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
Shun-Cheng Wu, Keisuke Tateno, Nassir Navab +1
Real-time scene reconstruction from depth data inevitably suffers from occlusion, thus leading to incomplete 3D models. Partial reconstructions, in turn, limit the performance of a…
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Helisa Dhamo, Keisuke Tateno, Iro Laina +2
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. This limi…
Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation
Yoshikatsu Nakajima, Keisuke Tateno, Federico Tombari +1
We propose an efficient and scalable method for incrementally building a dense, semantically annotated 3D map in real-time. The proposed method assigns class probabilities to each…
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Keisuke Tateno, Federico Tombari, Iro Laina +1
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…