1 code implementation • ECCV 2020 • Yiming Qian, Yasutaka Furukawa
This paper proposes a novel single-image piecewise planar reconstruction technique that infers and enforces inter-plane relationships.
no code implementations • 19 Apr 2024 • Ryo Yonetani, Jun Baba, Yasutaka Furukawa
We present RetailOpt, a novel opt-in, easy-to-deploy system for tracking customer movements in indoor retail environments.
no code implementations • 23 Mar 2024 • Jiacheng Chen, Yuefan Wu, Jiaqi Tan, Hang Ma, Yasutaka Furukawa
The paper further makes benchmark contributions by 1) Improving processing code for existing datasets to produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks.
no code implementations • 20 Feb 2024 • Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, Rakesh Ranjan
MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time.
no code implementations • 28 Jan 2024 • Xiang Xu, Joseph G. Lambourne, Pradeep Kumar Jayaraman, Zhengqing Wang, Karl D. D. Willis, Yasutaka Furukawa
Starting from the root and progressing to the leaf, BrepGen employs Transformer-based diffusion models to sequentially denoise node features while duplicated nodes are detected and merged, recovering the B-Rep topology information.
no code implementations • 30 Nov 2023 • Weilian Song, Jieliang Luo, Dale Zhao, Yan Fu, Chin-Yi Cheng, Yasutaka Furukawa
This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Modeling (BIM) applications.
1 code implementation • NeurIPS 2023 • Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, Yasutaka Furukawa
This paper introduces MVDiffusion, a simple yet effective method for generating consistent multi-view images from text prompts given pixel-to-pixel correspondences (e. g., perspective crops from a panorama or multi-view images given depth maps and poses).
1 code implementation • 30 Jun 2023 • Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne, Karl D. D. Willis, Yasutaka Furukawa
This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neural codes, from global part arrangement down to local curve geometry; and 2) controls the generation or completion of CAD models by specifying the target design using a code tree.
1 code implementation • NeurIPS 2023 • Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa
This paper presents PolyDiffuse, a novel structured reconstruction algorithm that transforms visual sensor data into polygonal shapes with Diffusion Models (DM), an emerging machinery amid exploding generative AI, while formulating reconstruction as a generation process conditioned on sensor data.
1 code implementation • CVPR 2023 • Ryuhei Hamaguchi, Yasutaka Furukawa, Masaki Onishi, Ken Sakurada
Conventional architectures encode entire scene contents at a fixed rate regardless of their temporal characteristics.
Ranked #2 on Object Detection on GEN1 Detection
1 code implementation • CVPR 2023 • Mohammad Amin Shabani, Sepidehsadat Hosseini, Yasutaka Furukawa
The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a single-step noise as the continuous quantity to precisely invert the continuous forward process; and 2) the final 2D coordinate as the discrete quantity to establish geometric incident relationships such as parallelism, orthogonality, and corner-sharing.
1 code implementation • CVPR 2023 • Shitao Tang, Sicong Tang, Andrea Tagliasacchi, Ping Tan, Yasutaka Furukawa
State-of-the-art feature matching methods require each scene to be stored as a 3D point cloud with per-point features, consuming several gigabytes of storage per scene.
no code implementations • 11 Jul 2022 • Xiang Xu, Karl D. D. Willis, Joseph G. Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, Yasutaka Furukawa
We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations.
no code implementations • 1 Jun 2022 • Sepidehsadat Hosseini, Yasutaka Furukawa
This paper presents an extreme floorplan reconstruction task, a new benchmark for the task, and a neural architecture as a solution.
1 code implementation • CVPR 2022 • Sachini Herath, David Caruso, Chen Liu, Yufan Chen, Yasutaka Furukawa
This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements.
1 code implementation • CVPR 2022 • Jiacheng Chen, Yiming Qian, Yasutaka Furukawa
This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure.
Edge Classification Extracting Buildings In Remote Sensing Images +1
1 code implementation • ICCV 2021 • Fuyang Zhang, Xiang Xu, Nelson Nauata, Yasutaka Furukawa
This paper presents an explore-and-classify framework for structured architectural reconstruction from an aerial image.
no code implementations • CVPR 2021 • Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang, Hang Chu, Chin-Yi Cheng, Yasutaka Furukawa
This paper proposes a generative adversarial layout refinement network for automated floorplan generation.
1 code implementation • CVPR 2021 • Madhawa Vidanapathirana, Qirui Wu, Yasutaka Furukawa, Angel X. Chang, Manolis Savva
We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene.
Ranked #1 on Plan2Scene on Rent3D++
1 code implementation • 18 May 2021 • Sachini Herath, Saghar Irandoust, Bowen Chen, Yiming Qian, Pyojin Kim, Yasutaka Furukawa
The paper proposes a multi-modal sensor fusion algorithm that fuses WiFi, IMU, and floorplan information to infer an accurate and dense location history in indoor environments.
1 code implementation • CVPR 2021 • Ryuhei Hamaguchi, Yasutaka Furukawa, Masaki Onishi, Ken Sakurada
This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a convolutional architecture.
1 code implementation • 3 Mar 2021 • Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang, Hang Chu, Chin-Yi Cheng, Yasutaka Furukawa
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation.
1 code implementation • ICCV 2021 • Mohammad Amin Shabani, Weilian Song, Makoto Odamaki, Hirochika Fujiki, Yasutaka Furukawa
We evaluate the proposed approach on a dataset of 1029 panorama images with 286 houses.
1 code implementation • CVPR 2021 • Yiming Qian, Hao Zhang, Yasutaka Furukawa
This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships.
1 code implementation • ECCV 2020 • Nelson Nauata, Kai-Hung Chang, Chin-Yi Cheng, Greg Mori, Yasutaka Furukawa
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture.
2 code implementations • ECCV 2020 • Nelson Nauata, Yasutaka Furukawa
This paper tackles a 2D architecture vectorization problem, whose task is to infer an outdoor building architecture as a 2D planar graph from a single RGB image.
1 code implementation • CVPR 2020 • Fuyang Zhang, Nelson Nauata, Yasutaka Furukawa
In our problem, nodes correspond to building edges in an image.
1 code implementation • ICCV 2019 • Jiacheng Chen, Chen Liu, Jiaye Wu, Yasutaka Furukawa
This paper proposes a new approach for automated floorplan reconstruction from RGBD scans, a major milestone in indoor mapping research.
3 code implementations • 30 May 2019 • Hang Yan, Sachini Herath, Yasutaka Furukawa
This paper sets a new foundation for data-driven inertial navigation research, where the task is the estimation of positions and orientations of a moving subject from a sequence of IMU sensor measurements.
1 code implementation • 12 Feb 2019 • Chen Liu, Yasutaka Furukawa
We propose a new approach for 3D instance segmentation based on sparse convolution and point affinity prediction, which indicates the likelihood of two points belonging to the same instance.
Ranked #1 on 3D Instance Segmentation on ScanNet
2 code implementations • CVPR 2019 • Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, Jan Kautz
This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces from a single RGB image.
no code implementations • ECCV 2018 • Huayi Zeng, Jiaye Wu, Yasutaka Furukawa
This paper proposes a novel 3D reconstruction approach, dubbed Neural Procedural Reconstruction (NPR), which trains deep neural networks to procedurally apply shape grammar rules and reconstruct CAD-quality models from 3D points.
no code implementations • CVPR 2018 • Luwei Yang, Feitong Tan, Ao Li, Zhaopeng Cui, Yasutaka Furukawa, Ping Tan
This paper presents a novel polarimetric dense monocular SLAM (PDMS) algorithm based on a polarization camera.
1 code implementation • CVPR 2018 • Chen Liu, Jimei Yang, Duygu Ceylan, Ersin Yumer, Yasutaka Furukawa
The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image.
Ranked #2 on Plane Instance Segmentation on NYU Depth v2
2 code implementations • ECCV 2018 • Chen Liu, Jiaye Wu, Yasutaka Furukawa
The ultimate goal of this indoor mapping research is to automatically reconstruct a floorplan simply by walking through a house with a smartphone in a pocket.
1 code implementation • ECCV 2018 • Hang Yan, Qi Shan, Yasutaka Furukawa
This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone.
1 code implementation • ICCV 2017 • Chen Liu, Jiajun Wu, Pushmeet Kohli, Yasutaka Furukawa
A neural architecture first transforms a rasterized image to a set of junctions that represent low-level geometric and semantic information (e. g., wall corners or door end-points).
1 code implementation • CVPR 2017 • Erik Wijmans, Yasutaka Furukawa
To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment.
no code implementations • CVPR 2017 • Hang Yan, Yebin Liu, Yasutaka Furukawa
Our approach first warps an input video into the viewpoint of a reference camera.
no code implementations • 5 Dec 2016 • Satoshi Ikehata, Ivaylo Boyadzhiev, Qi Shan, Yasutaka Furukawa
This paper addresses the problem of Structure from Motion (SfM) for indoor panoramic image streams, extremely challenging even for the state-of-the-art due to the lack of textures and minimal parallax.
no code implementations • 5 Dec 2016 • Chen Liu, Hang Yan, Pushmeet Kohli, Yasutaka Furukawa
This paper proposes a novel MAP inference framework for Markov Random Field (MRF) in parallel computing environments.
no code implementations • 5 Dec 2016 • Chen Liu, Jiajun Wu, Pushmeet Kohli, Yasutaka Furukawa
Our result implies that neural networks are effective at perceptual tasks that require long periods of reasoning even for humans to solve.
no code implementations • CVPR 2016 • Chen Liu, Pushmeet Kohli, Yasutaka Furukawa
This paper addresses the challenging problem of perceiving the hidden or occluded geometry of the scene depicted in any given RGBD image.
no code implementations • ICCV 2015 • Satoshi Ikehata, Hang Yang, Yasutaka Furukawa
The grammar then drives a principled new reconstruction algorithm, where the grammar rules are sequentially applied to recover a structured model.
no code implementations • CVPR 2014 • Ricardo Cabral, Yasutaka Furukawa
The second challenge is the need of a sophisti- cated regularization technique that enforces piecewise pla- narity, to suppress clutter and yield high quality texture mapped models.
no code implementations • CVPR 2014 • Qi Shan, Brian Curless, Yasutaka Furukawa, Carlos Hernandez, Steven M. Seitz
The proposed approach outperforms state of the art MVS techniques for challenging Internet datasets, yielding dramatic quality improvements both around object contours and in surface detail.