Search Results for author:
Found 24 papers, 9 papers with code
Sonata: Self-Supervised Learning of Reliable Point Representations
In this paper, we question whether we have a reliable self-supervised point cloud model that can be used for diverse 3D tasks via simple linear probing, even with limited data and minimal computation.
Ranked #1 on
Semantic Segmentation
on S3DIS
(using extra training data)
EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
The advent of wearable computers enables a new source of context for AI that is embedded in egocentric sensor data.
Ranked #1 on
3D Reconstruction
on Aria Synthetic Environments
EgoLifter: Open-world 3D Segmentation for Egocentric Perception
In this paper we present EgoLifter, a novel system that can automatically segment scenes captured from egocentric sensors into a complete decomposition of individual 3D objects.
Pixel-Aligned Recurrent Queries for Multi-View 3D Object Detection
We present PARQ - a multi-view 3D object detector with transformer and pixel-aligned recurrent queries.
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.
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.
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e. g. urban driving scenes.
3D Object Detection
3D Object Detection From Monocular Images
+3
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.
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.
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.
Analyzing Visual Representations in Embodied Navigation Tasks
Recent advances in deep reinforcement learning require a large amount of training data and generally result in representations that are often over specialized to the target task.
Insights on Visual Representations for Embodied Navigation Tasks
Surprisingly, we find that slight differences in task have no measurable effect on the visual representation for both SqueezeNet and ResNet architectures.
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.
Habitat: A Platform for Embodied AI Research
We present Habitat, a platform for research in embodied artificial intelligence (AI).
Ranked #2 on
PointGoal Navigation
on Gibson PointGoal Navigation
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.
Bayesian Nonparametric Modeling of Driver Behavior using HDP Split-Merge Sampling Algorithm
In a novel manner, we demonstrate how the sparsity of the personal road network of a driver in conjunction with a hierarchical topic model allows data driven predictions about destinations as well as likely road conditions.
Direction-Aware Semi-Dense SLAM
To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding.
Small-Variance Nonparametric Clustering on the Hypersphere
Based on the small-variance limit of Bayesian nonparametric von-Mises-Fisher (vMF) mixture distributions, we propose two new flexible and efficient k-means-like clustering algorithms for directional data such as surface normals.
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction.
Semantically-Aware Aerial Reconstruction From Multi-Modal Data
We consider a methodology for integrating multiple sensors along with semantic information to enhance scene representations.
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models.
A Mixture of Manhattan Frames: Beyond the Manhattan World
Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system.
