【3D大數據庫助力機器人了解真實世界】

【3D大數據庫助力機器人了解真實世界】2019年計算機視覺與圖像處理應用國際會議(CVPR)上,來自斯坦福、英特爾等組成的研究團隊宣布推出PartNet:一個龐大的常見3D對象數據庫,按分類與注釋集合萬余常見對象。作為一個新的常見對象語義數據庫,將為機器人帶來全新的現實理解。http://www.eatvvt.tw/?p=66967

Massive 3D Dataset Helps Robots Understand What Things Are

PartNet is a new semantic database of common objects that brings a new level of real-world understanding to robots

One of the things that makes humans so great at adapting to the world around us is our ability to understand entire categories of things all at once, and then use that general understanding to

One of the things that makes humans so great at adapting to the world around us is our ability to understand entire categories of things all at once, and then use that general understanding to make sense of specific things that we’ve never seen before. For example, consider something like a lamp. We’ve all seen some lamps. Nobody has seen every single lamp there is. But in most cases, we can walk into someone’s house for the first time and easily identify all their lamps and how they work. Every once in a while, of course, there will be something?incredibly weird?that’ll cause you to have to ask, “Uh, is that a lamp? How do I turn it on?” But most of the time, our generalized mental model of lamps keeps us out of trouble.

It’s helpful that lamps, along with other categories of objects, have (by definition) lots of pieces in common with each other. Lamps usually have bulbs in them. They often have shades. There’s probably also a base to keep it from falling over, a body to get it off the ground, and a power cord. If you see something with all of those characteristics, it’s probably a lamp, and once you know that, you can make educated guesses about how to usefully interact with it.

This level of understanding is something that robots tend to be particularly bad at, which is a real shame because of how useful it is. You might even argue that robots will have to understand objects on a level close to this if we’re ever going to trust them to operate autonomously in unstructured environments. At the?2019 Conference on Computer Vision and Pattern Recognition?(CVPR) this week, a group of researchers from Stanford, UCSD, SFU, and Intel are announcing?PartNet, a huge database of common 3D objects that are broken down and annotated at the level required to, they hope, teach a robot exactly what a lamp is.

PartNet: Database of 3D objects to help robots
Image: PartNet Project
Example shapes with fine-grained part annotations for the 24 object categories in the PartNet dataset.

PartNet is a subset of?ShapeNet, an even huger 3D database of over 50,000 common objects. PartNet has 26,671 objects in its database across 24 categories (like doors, tables, chairs, lamps, microwaves, and clocks), and each one of those objects has been broken down into labeled component parts. Here’s what that looks like for two totally different looking lamps:

PartNet category: Lamp
Image: PartNet
PartNet features an expert-defined hierarchical template for each of its categories, like lamp (middle). This template includes different object types like a table lamp (left) and a ceiling lamp (right). The template is designed to be deep and comprehensive to cover structurally different types of lamps, with the same part concepts, such as a light bulb and lamp shade, are shared across the different types.

All that semantically labeled detail is what makes PartNet special. Databases like ShapeNet basically just say “here are a bunch of things that are lamps,” which has limited usefulness. PartNet, by contrast, is a way to much more fundamentally understand lamps: What parts they’re made of, where controls tend to be, and so on. Beyond just helping with a much more generalized identification of previously unseen lamps, it also makes it possible for an autonomous system (with the proper training) to make inferences about how to interact with those unseen lamps in productive ways.

As you might expect, creating PartNet was a stupendous amount of work. Nearly 70 “professional annotators” spent an average of 8 minutes annotating each and every one of those 26,671 3D shapes with a total of 573,585 parts, and then each annotation was verified at least once by another annotator. To keep things consistent, templates were created for each class of object, with the goal of minimizing the set of parts in a way that still comprehensively covered everything necessary to describe the entire object class. The parts are organized hierarchically, too, with small parts a subset of larger ones. Here’s how it all breaks down:

In order for this to be useful outside of PartNet itself, robots will have to be able to do the 3D segmentation step on their own, taking 3D models of objects (that the robot creates) and then breaking them down into pieces that can be identified and correlated with the existing object models. This is a tricky thing to do for a bunch of reasons: For example, you need to be able to identify individual parts from point clouds that may be small but also important (like drawer pulls and door knobs), and many parts that look visually similar may be semantically quite different.

The researchers have made some progress on this, but it’s still an area that needs more work. And that’s what PartNet is for, too—providing a dataset that can be used to develop better algorithms. At some point, PartNet may be part of a foundation for systems that can even annotate similar 3D models completely on their own, in the same way that we’ve seen autonomous driving datasets transition from human annotation to automatic annotation with human supervision. Bringing that level of semantic understanding to unfamiliar and unstructured environments will be key to those real-world adaptable robots that always seem to be right around the corner.

PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding,” by Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su from Stanford University, University of California San Diego, Simon Fraser University, and Intel AI Lab,? was presented at the 2019 Conference on Computer Vision and Pattern Recognition.

[?PartNet?]

https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/partnet-helps-robots-understand-what-things-are


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