Search Results for author:
Found 32 papers, 13 papers with code
FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation
Estimating relative camera poses between images has been a central problem in computer vision.
LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent
While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline.
Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data
We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data.
Understanding 3D Object Interaction from a Single Image
Humans can easily understand a single image as depicting multiple potential objects permitting interaction.
MOVES: Manipulated Objects in Video Enable Segmentation
We present a method that uses manipulation to learn to understand the objects people hold and as well as hand-object contact.
Perspective Fields for Single Image Camera Calibration
We propose perspective fields as a representation that models the local perspective properties of an image.
Large-Scale Spatial Cross-Calibration of Hinode/SOT-SP and SDO/HMI
Second, analysis of over 12, 000 scans show that the pointing information is often incorrect by dozens of arcseconds with a strong bias.
The 8-Point Algorithm as an Inductive Bias for Relative Pose Prediction by ViTs
We present a simple baseline for directly estimating the relative pose (rotation and translation, including scale) between two images.
PlaneFormers: From Sparse View Planes to 3D Reconstruction
We present an approach for the planar surface reconstruction of a scene from images with limited overlap.
Sound Localization by Self-Supervised Time Delay Estimation
We adapt the contrastive random walk of Jabri et al. to learn a cycle-consistent representation from unlabeled stereo sounds, resulting in a model that performs on par with supervised methods on "in the wild" internet recordings.
Understanding 3D Object Articulation in Internet Videos
We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary videos.
What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction
We present an approach for full 3D scene reconstruction from a single unseen image.
Recognizing Scenes from Novel Viewpoints
Humans can perceive scenes in 3D from a handful of 2D views.
SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory
Both NASA's Solar Dynamics Observatory (SDO) and the JAXA/NASA Hinode mission include spectropolarimetric instruments designed to measure the photospheric magnetic field.
PixelSynth: Generating a 3D-Consistent Experience from a Single Image
Recent advancements in differentiable rendering and 3D reasoning have driven exciting results in novel view synthesis from a single image.
Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry?
At the heart of our approach is the idea of collision replay, where we use examples of a collision to provide supervision for observations at a past frame.
Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification
The Helioseismic and Magnetic Imager (HMI) onboard NASA's Solar Dynamics Observatory (SDO) produces estimates of the photospheric magnetic field which are a critical input to many space weather modelling and forecasting systems.
Planar Surface Reconstruction from Sparse Views
The paper studies planar surface reconstruction of indoor scenes from two views with unknown camera poses.
Full-Body Awareness from Partial Observations
There has been great progress in human 3D mesh recovery and great interest in learning about the world from consumer video data.
Associative3D: Volumetric Reconstruction from Sparse Views
This information is then jointly reasoned over to produce the most likely explanation of the scene.
Understanding Human Hands in Contact at Internet Scale
Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands has the potential to pave the way to systems that can learn from petabytes of video data.
Novel Object Viewpoint Estimation through Reconstruction Alignment
Our key insight is that although we do not have an explicit 3D model or a predefined canonical pose, we can still learn to estimate the object's shape in the viewer's frame and then use an image to provide our reference model or canonical pose.
Articulation-aware Canonical Surface Mapping
We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape, and 2) inferring the articulation and pose of the template corresponding to the input image.
A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research.
From Lifestyle Vlogs to Everyday Interactions
A major stumbling block to progress in understanding basic human interactions, such as getting out of bed or opening a refrigerator, is lack of good training data.
From Images to 3D Shape Attributes
Our first objective is to infer these 3D shape attributes from a single image.
3D Shape Attributes
In this paper we investigate 3D attributes as a means to understand the shape of an object in a single image.
Learning a Predictable and Generative Vector Representation for Objects
The network consists of two components: (a) an autoencoder that ensures the representation is generative; and (b) a convolutional network that ensures the representation is predictable.
Single Image 3D Without a Single 3D Image
Do we really need 3D labels in order to learn how to predict 3D?
In Defense of the Direct Perception of Affordances
The field of functional recognition or affordance estimation from images has seen a revival in recent years.
Designing Deep Networks for Surface Normal Estimation
We show by incorporating several constraints (man-made, manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation.
Predicting Object Dynamics in Scenes
Given a static scene, a human can trivially enumerate the myriad of things that can happen next and characterize the relative likelihood of each.
