Research

Computer Vision

Reconstructing Scenes with Mirror and Glass Surfaces

July 7, 2018

Abstract

Planar reflective surfaces such as glass and mirrors are notoriously hard to reconstruct for most current 3D scanning techniques. When treated naïvely, they introduce duplicate scene structures, effectively destroying the reconstruction altogether. Our key insight is that an easy to identify structure attached to the scanner—in our case an AprilTag—can yield reliable information about the existence and the geometry of glass and mirror surfaces in a scene. We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two. Furthermore, our system can automatically segment observations of multiple reflective surfaces in a scene based on their estimated planes and locations. In the proposed setup, minimal additional hardware is needed to create high-quality results. We demonstrate this using reconstructions of several scenes with a variety of real mirrors and glass.

Download the Paper

Related Publications

July 13, 2026

S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval

Xiaodong Wang, Xuanyi Zhao, Pedro Rodriguez, Devendra Singh Sachan, Barlas Oguz, Seungwhan Moon, Shang-Wen Li, Gargi Ghosh, Xin Dong, Wen-Tau Yih

July 13, 2026

July 03, 2026

Human & Machine Intelligence

Robotics

Interpreting Physics in Video World Models

Sonia Joseph, Quentin Garrido, Randall Balestriero, Matthew Kowal, Thomas Fel, Shahab Bakhtiari, Blake Richards, Mike Rabbat

July 03, 2026

May 26, 2026

Human & Machine Intelligence

Theory

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images

Josephine Raugel, Max Seitzer, Marc Szafraniec, Huy V. Vo, Jérémy Rapin, Patrick Labatut, Piotr Bojanowski, Valentin Wyart, Jean Remi King

May 26, 2026

May 19, 2026

Human & Machine Intelligence

EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data

Dongyan Lin, Phillip Rust, Angel Villar Corrales, Alvin W. M. Tan, Mahi Luthra, Charles-Eric Saint-James, Rashel Moritz, Sheila Krogh-Jespersen, Vanessa Stark, Surya Parimi, Jiayi Shen, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Tom Fizycki, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Juan Pino, Michael C. Frank, Emmanuel Dupoux

May 19, 2026

June 11, 2019

Computer Vision

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | Facebook AI Research

Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick

June 11, 2019

April 30, 2018

NLP

Computer Vision

Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research

Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston

April 30, 2018

October 10, 2016

Speech & Audio

Computer Vision

Polysemous Codes | Facebook AI Research

Matthijs Douze, Hervé Jégou, Florent Perronnin

October 10, 2016

June 18, 2018

Speech & Audio

Computer Vision

Low-shot learning with large-scale diffusion | Facebook AI Research

Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou

June 18, 2018

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.