384 citations · 493 across the 20 of their papers we have counts for
20 papers · 1 filter
Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation
Tanner Schmidt, Richard Newcombe
This paper presents Segment This Thing (STT), a new efficient image segmentation model designed to produce a single segment given a single point prompt. Instead of following prior…
EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
Julian Straub, Daniel DeTone, Tianwei Shen +3
The advent of wearable computers enables a new source of context for AI that is embedded in egocentric sensor data. This new egocentric data comes equipped with fine-grained 3D loc…
Aria Everyday Activities Dataset
Zhaoyang Lv, Nicholas Charron, Pierre Moulon +21
We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by…
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Kristen Grauman, Andrew Westbury, Lorenzo Torresani +98
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric…
EgoBlur: Responsible Innovation in Aria
Nikhil Raina, Guruprasad Somasundaram, Kang Zheng +12
Project Aria pushes the frontiers of Egocentric AI with large-scale real-world data collection using purposely designed glasses with privacy first approach. To protect the privacy…
Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
Xiaqing Pan, Nicholas Charron, Yongqian Yang +6
We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured using Aria glasses with extensive object, environment, and human level ground truth. This ADT release cont…
EgoHumans: An Egocentric 3D Multi-Human Benchmark
Rawal Khirodkar, Aayush Bansal, Lingni Ma +3
We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking. Existing egocentric benchmar…
OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
Paul-Edouard Sarlin, Daniel DeTone, Tsun-Yi Yang +7
Humans can orient themselves in their 3D environments using simple 2D maps. Differently, algorithms for visual localization mostly rely on complex 3D point clouds that are expensiv…
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
Gil Avraham, Julian Straub, Tianwei Shen +7
This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representatio…
Self-supervised Neural Articulated Shape and Appearance Models
Fangyin Wei, Rohan Chabra, Lingni Ma +6
Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches ha…
LISA: Learning Implicit Shape and Appearance of Hands
Enric Corona, Tomas Hodan, Minh Vo +4
This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide de…
ERF: Explicit Radiance Field Reconstruction From Scratch
Samir Aroudj, Steven Lovegrove, Eddy Ilg +3
We propose a novel explicit dense 3D reconstruction approach that processes a set of images of a scene with sensor poses and calibrations and estimates a photo-real digital model.…
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Kristen Grauman, Andrew Westbury, Eugene Byrne +82
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outd…
ODAM: Object Detection, Association, and Mapping using Posed RGB Video
Kejie Li, Daniel DeTone, Steven Chen +6
Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics.…
FroDO: From Detections to 3D Objects
Kejie Li, Martin Rünz, Meng Tang +8
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg +4
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a…
The Replica Dataset: A Digital Replica of Indoor Spaces
Julian Straub, Thomas Whelan, Lingni Ma +27
We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-d…
StereoDRNet: Dilated Residual Stereo Net
Rohan Chabra, Julian Straub, Chris Sweeney +2
We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reco…
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Jeong Joon Park, Peter Florence, Julian Straub +2
Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-o…
Surface Light Field Fusion
Jeong Joon Park, Richard Newcombe, Steve Seitz
We present an approach for interactively scanning highly reflective objects with a commodity RGBD sensor. In addition to shape, our approach models the surface light field, encodin…