Softwares and Datasets

The GAMMA research group has a variety of software and models available to download.

BOOLE: Constructive solid geometry (CSG) models into boundary representation (B-rep) form
Fast back-face culling using normal masks
IK: Inverse kinematics code for serial manipulators
MAPC: Efficient and exact manipulation of algebraic points and curves
MARS: Maple/MATLAB/C resultant-based solver
Polygon Triangulation: Fast polygon triangulation based on Seidel’s algorithm

Behavior-Driven Traffic Simulator: A traffic simulation software based on SUMO that incorporates driver behavior
TrackNPred: Implementations of state-of-the-art tracking and trajectory prediction methods and tools to benchmark and evaluate them on real-world dense traffic datasets
TRAF: Dataset of dense and heterogeneous traffic videos, consisting of multiple camera viewpoints (front-facing/top-view), camera motions (moving/static), times of day, and agents (car, bus, truck, rickshaw, pedestrian, scooter, motorcycle)
METEOR: A new and complex traffic dataset for unstructured scenarios in India.

DEEP: Dual-space expansion for estimating penetration depth
DEFORMCD: Collision detection for deforming objects
FCL: A fast collision library and its integration with ROS for use with robotic systems
MCCD: A library for multi-core collision detection
gProximity: Hierarchical GPU-based operations for collision and distance queries
PIVOT2D: Proximity information from Voronoi techniques
PQP: Fast proximity queries with swept sphere volumes
RAPID: Robust and accurate polygon interference detection
SELF-CCD: Continuous collision detection for deforming objects
BSC: Reliable continuous collision detection
SWIFT: Speedy walking via improved feature testing
SWIFT++: Speedy walking via improved feature testing for non-convex objects
V-COLLIDE: Accelerated collision detection for VRML

DeceptiveWalk: Dataset of 16-joint gaits representing deceptive and non-deceptive walking
GroupWalk: Dataset of walking crowd videos collected in eight real-world locations
TAEW: A semi-supervised method to detect one or more perceived emotions in gaits, between happy, sad, angry, and neutral
STEP: A method to both generate synthetic emotive gaits and classify emotive gaits into one of four emotions, happy, sad, angry, and neutral
Emotion-Gait: Dataset of 21-joint gaits annotated with four perceived emotions, happy, sad, angry, and neutral
EmotionWalk: Dataset of 16-joint gaits annotated with four perceived emotions, happy, sad, angry and neutral

GPUFFTW: High performance power-of-two fast Fourier transforms (FFT) using graphics processors
GPUSORT: High performance sorting using graphics processors
HAVOC3D: Fast distance field and Voronoi diagram computation using graphics hardware
LU-GPU: Efficient algorithms for solving dense linear systems on graphics hardware

ITOMP Library: Realtime optimization based High-DOF planner for dynamic scenes
HRVO Library: Independent navigation of multiple robots or virtual agents using hybrid reciprocal velocity obstacles
RVO2 Library: Reciprocal collision avoidance for real-time multi-agent simulation
D-ORCA: Distributed Optimal Reciprocal Collision Avoidance

GSound: Fast sound propagation and rendering for games
pygsound: Python package for impulse response generation based on GSound
FAST-RIR: Python package for neural-network-based fast diffuse room impulse response generator
MESH2IR: Python package for mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh
TS-RIR: Python package to translate synthetic RIR to real RIR
6-channel synthetic IR dataset: Dataset used in our paper Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
Low-frequency compensated synthetic IR dataset: Dataset used in our paper: Low-frequency Compensated Synthetic Impulse Responses for Improved Far-field Speech Recognition

VideoSham: Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis