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Found 18 papers, 7 papers with code
EgoBlur: Responsible Innovation in Aria
Project Aria pushes the frontiers of Egocentric AI with large-scale real-world data collection using purposely designed glasses with privacy first approach.
Project Aria: A New Tool for Egocentric Multi-Modal AI Research
Egocentric, multi-modal data as available on future augmented reality (AR) devices provides unique challenges and opportunities for machine perception.
Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured using Aria glasses with extensive object, environment, and human level ground truth.
EgoHumans: An Egocentric 3D Multi-Human Benchmark
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.
OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
We bridge this gap by introducing OrienterNet, the first deep neural network that can localize an image with sub-meter accuracy using the same 2D semantic maps that humans use.
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism.
Self-supervised Neural Articulated Shape and Appearance Models
In addition, our representation enables a large variety of applications, such as few-shot reconstruction, the generation of novel articulations, and novel view-synthesis.
LISA: Learning Implicit Shape and Appearance of Hands
This paper proposes a do-it-all neural model of human hands, named LISA.
ERF: Explicit Radiance Field Reconstruction From Scratch
Robustly reconstructing such a volumetric scene model with millions of unknown variables from registered scene images only is a highly non-convex and complex optimization problem.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
ODAM: Object Detection, Association, and Mapping using Posed RGB Video
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.
Neural 3D Video Synthesis from Multi-view Video
We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation.
FroDO: From Detections to 3D Objects
We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner.
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception.
The Replica Dataset: A Digital Replica of Indoor Spaces
We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale.
StereoDRNet: Dilated Residual Stereo Net
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 reconstruction of a scene.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
Surface Light Field Fusion
We present an approach for interactively scanning highly reflective objects with a commodity RGBD sensor.
