We introduce RigidFusion. A RGB-D reconstruction system targets on dynamic environments without using object priors.
Multi-object tracking with deep priors using RGB-D input. Our key insight is geometry completion helps tracking.
A goal-oriented meaning-based statistical framework is presented to solve the math word problem. Our system is able to generate a reasonable explanation.
We present SMARTANNOTATOR, an interactive system to facilitate annotating raw RGBD images. The system performs the tedious tasks and iteratively refines users’ annotations.
This toolbox implements common machine learning methods in MATLAB, including support vector machine (SVM) with sequential minimal optimization, Laplacian SVM, spectral clustering.
In this project, we developed a system that support 3D object composition. It was integrated into Blender via Blender’s Python API to add special visual effects.
Youtube Video I - Object Compostion
Youtube Video II - VFX
This is a web-based tool that allows users to annotate 2D images. We used this tools to collect over ninety annotations. This tool was further integrated to our single- view reconstruction system.
This renderer employs fixed-function rendering pipeline and supports: triangle rasterization, textures sampling, mipmap, and bump mapping.