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Massachusetts Institute of Technology

Machines' perception of space

Abstract

dc:description.abstract

Architectural design is highly dependent on the architect's understanding of space. However, in the era of digital revolution, when efficiency and economy are the major concerns in most industrial fields, whether a computer can gain human-like understanding to read and operate space and assist with its design and analysis remains a question. This thesis focuses on the geometrical aspects of spatial awareness. Machine systems that have similar behaviors to humans' perceptions of space in geometric aspects will be developed employing techniques such as isovist and machine learning, and trained with open-sourced datasets, self-generated datasets or crowdsourced datasets. The proposed systems simulate behaviors including space composition classification, space scene classification, 3D reconstruction of space, space rating and algebraic operations of space. These aspects cover topics ranging from pure geometrical understandings to semantic reasoning and emotional feelings of space. The proposed systems are examined in two ways. Firstly, they are applied to a real-time space evaluation modeling interface, which gives a user instant insights about the scene being constructed; Secondly, they are also undertaken in the spatial analysis of existing architectural designs, namely small designs by Mies van der Rohe and Aldo van Eyck. The case studies conducted validate that this methodology works well in understanding local spatial conditions, and that it can be helpful either as a design aid tool or in spatial analysis.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Architecture.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Peng, Wenzhe
Advisor dc:contributor.advisor
  • Takehiko Nagakura.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/118574
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/118574

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Peng, Wenzhe. Machines' perception of space. Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118574