Search Results for author:
Found 11 papers, 3 papers with code
Fast Sparse View Guided NeRF Update for Object Reconfigurations
Neural Radiance Field (NeRF), as an implicit 3D scene representation, lacks inherent ability to accommodate changes made to the initial static scene.
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D
However, the SDS method is also the source of several artifacts, such as the Janus problem, the misalignment between the text prompt and the generated 3D model, and 3D model inaccuracies.
Towards Visual Foundational Models of Physical Scenes
We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion.
Single View Physical Distance Estimation using Human Pose
We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video captured by a camera viewing a 3-D scene from a fixed vantage point.
An Adaptive Framework for Learning Unsupervised Depth Completion
We present a method to infer a dense depth map from a color image and associated sparse depth measurements.
Unsupervised Depth Completion from Visual Inertial Odometry
Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points.
Ranked #4 on
Depth Completion
on VOID
Geo-Supervised Visual Depth Prediction
We propose using global orientation from inertial measurements, and the bias it induces on the shape of objects populating the scene, to inform visual 3D reconstruction.
Visual-Inertial Object Detection and Mapping
We present a method to populate an unknown environment with models of previously seen objects, placed in a Euclidean reference frame that is inferred causally and on-line using monocular video along with inertial sensors.
Visual-Inertial-Semantic Scene Representation for 3D Object Detection
We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones.
Visual-Inertial-Semantic Scene Representation for 3-D Object Detection
We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones.
A Simple Hierarchical Pooling Data Structure for Loop Closure
We propose a data structure obtained by hierarchically averaging bag-of-word descriptors during a sequence of views that achieves average speedups in large-scale loop closure applications ranging from 4 to 20 times on benchmark datasets.
