Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets, limiting their application to a narrow spectrum of object categories.
Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation.
Our key idea is using primitive graph as a unified representation of vector maps and formulating shape regularization and topology reconstruction as primitive graph reconstruction problems that can be solved in the same framework.
With such frames, we can factorize geometry and motion to facilitate a feature-space geometric reconstruction for more effective 4D learning.
Then, we convert vector shape prediction, regularization, and topology reconstruction into a unique primitive graph learning problem.
This paper proposes, implements, and evaluates a reinforcement learning (RL)-based computational framework for automatic mesh generation.
We jointly train a layout module to produce an initial layout and a novel MVS module to obtain accurate layout geometry.
We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks.
This paper presents our research on leveraging social media Big Data and AI to support hurricane disaster emergency response.
We present PrimitiveNet, a novel approach for high-resolution primitive instance segmentation from point clouds on a large scale.
As a result, we achieve promising results on all datasets and the highest F-Score on the online TNT intermediate benchmark.
Ranked #8 on Point Clouds on Tanks and Temples
Such a space naturally allows the disentanglement of geometric style (coming from the source) and structural pose (conforming to the target).
We present HoliCity, a city-scale 3D dataset with rich structural information.
We illustrate the effectiveness of this learned deformation space for various downstream applications, including shape generation via deformation, geometric style transfer, unsupervised learning of a consistent parameterization for entire classes of shapes, and shape interpolation.
Given a pair of shapes, our framework provides a novel shape feature-preserving mapping function that continuously deforms one model to the other by minimizing fitting and rigidity losses based on the non-rigid iterative-closest-point (ICP) algorithm.
Graphics Computational Geometry
We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task.
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
In this work, we present a novel approach for color texture generation using a conditional adversarial loss obtained from weakly-supervised views.
We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images.
In this work, we introduce the novel problem of identifying dense canonical 3D coordinate frames from a single RGB image.
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Ranked #2 on 6D Pose Estimation using RGBD on CAMERA25
We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.
Ranked #24 on Semantic Segmentation on Stanford2D3D Panoramic
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e. g., color texture maps).
Ranked #20 on Semantic Segmentation on ScanNet
We present an automatic thumbnail generation technique based on two essential considerations: how well they visually represent the original photograph, and how well the foreground can be recognized after the cropping and downsizing steps of thumbnailing.