SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors

08/12/2020
by   Mona Jalal, et al.
0

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.

READ FULL TEXT

page 1

page 2

research
04/18/2018

Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation

We present a new dataset, called Falling Things (FAT), for advancing the...
research
09/21/2021

StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation

We present a large-scale stereo RGB image object pose estimation dataset...
research
09/29/2016

Pano2CAD: Room Layout From A Single Panorama Image

This paper presents a method of estimating the geometry of a room and th...
research
07/28/2020

EXPO-HD: Exact Object Perception using High Distraction Synthetic Data

We present a new labeled visual dataset intended for use in object detec...
research
02/18/2016

Weighted Unsupervised Learning for 3D Object Detection

This paper introduces a novel weighted unsupervised learning for object ...
research
07/10/2021

SynPick: A Dataset for Dynamic Bin Picking Scene Understanding

We present SynPick, a synthetic dataset for dynamic scene understanding ...
research
03/24/2023

ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data

Compared with traditional RGB-only visual tracking, few datasets have be...

Please sign up or login with your details

Forgot password? Click here to reset