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Angel Chang

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5 papers

1 author row

NeurIPS Conference 2023 Conference Paper

A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset

Zahra Gharaee
ZeMing Gong
Nicholas Pellegrino
Iuliia Zarubiieva
Joakim Bruslund Haurum
Scott Lowe
Jaclyn McKeown
Chris Ho

In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-1M Insect Dataset. Each record is taxonomically classified by an expert, and also has associated genetic information including raw nucleotide barcode sequences and assigned barcode index numbers, which are genetic-based proxies for species classification. This paper presents a curated million-image dataset, primarily to train computer-vision models capable of providing image-based taxonomic assessment, however, the dataset also presents compelling characteristics, the study of which would be of interest to the broader machine learning community. Driven by the biological nature inherent to the dataset, a characteristic long-tailed class-imbalance distribution is exhibited. Furthermore, taxonomic labelling is a hierarchical classification scheme, presenting a highly fine-grained classification problem at lower levels. Beyond spurring interest in biodiversity research within the machine learning community, progress on creating an image-based taxonomic classifier will also further the ultimate goal of all BIOSCAN research: to lay the foundation for a comprehensive survey of global biodiversity. This paper introduces the dataset and explores the classification task through the implementation and analysis of a baseline classifier. The code repository of the BIOSCAN-1M-Insect dataset is available at https: //github. com/zahrag/BIOSCAN-1M

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NeurIPS Conference 2022 Conference Paper

MultiScan: Scalable RGBD scanning for 3D environments with articulated objects

Yongsen Mao
Yiming Zhang
Hanxiao Jiang
Angel Chang
Manolis Savva

We introduce MultiScan, a scalable RGBD dataset construction pipeline leveraging commodity mobile devices to scan indoor scenes with articulated objects and web-based semantic annotation interfaces to efficiently annotate object and part semantics and part mobility parameters. We use this pipeline to collect 273 scans of 117 indoor scenes containing 10957 objects and 5129 parts. The resulting MultiScan dataset provides RGBD streams with per-frame camera poses, textured 3D surface meshes, richly annotated part-level and object-level semantic labels, and part mobility parameters. We validate our dataset on instance segmentation and part mobility estimation tasks and benchmark methods for these tasks from prior work. Our experiments show that part segmentation and mobility estimation in real 3D scenes remain challenging despite recent progress in 3D object segmentation.

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NeurIPS Conference 2021 Conference Paper

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

Andrew Szot
Alexander Clegg
Eric Undersander
Erik Wijmans
Yili Zhao
John Turner
Noah Maestre
Mustafa Mukadam

We introduce Habitat 2. 0 (H2. 0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack – data, simulation, and benchmark tasks. Specifically, we present: (i) ReplicaCAD: an artist-authored, annotated, reconfigurable 3D dataset of apartments (matching real spaces) with articulated objects (e. g. cabinets and drawers that can open/close); (ii) H2. 0: a high-performance physics-enabled 3D simulator with speeds exceeding 25, 000 simulation steps per second (850x real-time) on an 8-GPU node, representing 100x speed-ups over prior work; and, (iii) Home Assistant Benchmark (HAB): a suite of common tasks for assistive robots (tidy the house, stock groceries, set the table) that test a range of mobile manipulation capabilities. These large-scale engineering contributions allow us to systematically compare deep reinforcement learning (RL) at scale and classical sense-plan-act (SPA) pipelines in long-horizon structured tasks, with an emphasis on generalization to new objects, receptacles, and layouts. We find that (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) a hierarchy with independent skills suffers from ‘hand-off problems’, and (3) SPA pipelines are more brittle than RL policies.

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NeurIPS Conference 2021 Conference Paper

Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

Santhosh Kumar Ramakrishnan
Aaron Gokaslan
Erik Wijmans
Oleksandr Maksymets
Alexander Clegg
John Turner
Eric Undersander
Wojciech Galuba

We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1, 000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-ﬂoor residences, stores, and other private indoor spaces. HM3D surpasses existing datasets available for academic research in terms of physical scale, completeness of the reconstruction, and visual ﬁdelity. HM3D contains 112. 5k m^2 of navigable space, which is 1. 4 - 3. 7× larger than other building-scale datasets (MP3D, Gibson). When compared to existing photorealistic 3D datasets (Replica, MP3D, Gibson, ScanNet), rendered images from HM3D have 20 - 85% higher visual ﬁdelity w. r. t. counterpart images captured with real cameras, and HM3D meshes have 34 - 91% fewer artifacts due to incomplete surface reconstruction. The increased scale, ﬁdelity, and diversity of HM3D directly impacts the performance of embodied AI agents trained using it. In fact, we ﬁnd that HM3D is ‘pareto optimal’ in the following sense – agents trained to perform PointGoal navigation on HM3D achieve the highest performance regardless of whether they are evaluated on HM3D, Gibson, or MP3D. No similar claim can be made about training on other datasets. HM3D-trained PointNav agents achieve 100% performance on Gibson-test dataset, suggesting that it might be time to retire that episode dataset. The HM3D dataset, analysis code, and pre-trained models are publicly released: https: //aihabitat. org/datasets/hm3d/.

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NeurIPS Conference 2020 Conference Paper

MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation

Saim Wani
Shivansh Patel
Unnat Jain
Angel Chang
Manolis Savva

Navigation tasks in photorealistic 3D environments are challenging because they require perception and effective planning under partial observability. Recent work shows that map-like memory is useful for long-horizon navigation tasks. However, a focused investigation of the impact of maps on navigation tasks of varying complexity has not yet been performed. We propose the multiON task, which requires navigation to an episode-specific sequence of objects in a realistic environment. MultiON generalizes the ObjectGoal navigation task and explicitly tests the ability of navigation agents to locate previously observed goal objects. We perform a set of multiON experiments to examine how a variety of agent models perform across a spectrum of navigation task complexities. Our experiments show that: i) navigation performance degrades dramatically with escalating task complexity; ii) a simple semantic map agent performs surprisingly well relative to more complex neural image feature map agents; and iii) even oracle map agents achieve relatively low performance, indicating the potential for future work in training embodied navigation agents using maps.

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