Carnegie Mellon University（卡内基梅隆大学）；Facebook Reality Labs（Facebook现实实验室）；Facebook AI Research（Facebook人工智能研究院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Senthil Purushwalkam, Sebastian Vicenc Amengual Gari, Vamsi Krishna Ithapu, Carl Schissler, Philip Robinson, Abhinav Gupta, Kristen Grauman
Given only a few glimpses of an environment, how much can we infer about its entire floorplan? Existing methods can map only what is visible or immediately apparent from context, and thus require substantial movements through a space to fully map it. We explore how both audio and visual sensing together can provide rapid floorplan reconstruction from limited viewpoints. Audio not only helps sense geometry outside the camera's field of view, but it also reveals the existence of distant freespace (e.g., a dog barking in another room) and suggests the presence of rooms not visible to the camera (e.g., a dishwasher humming in what must be the kitchen to the left). We introduce AV-Map, a novel multi-modal encoder-decoder framework that reasons jointly about audio and vision to reconstruct a floorplan from a short input video sequence. We train our model to predict both the interior structure of the environment and the associated rooms' semantic labels. Our results on 85 large real-world environments show the impact: with just a few glimpses spanning 26% of an area, we can estimate the whole area with 66% accuracy -- substantially better than the state of the art approach for extrapolating visual maps.
Washington University in St. Louis（圣路易斯华盛顿大学）；Simon Fraser University（西蒙弗雷泽大学）
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. This paper tackles this problem by proposing FloorNet, a novel deep neural architecture. The challenge lies in the processing of RGBD streams spanning a large 3D space. FloorNet effectively processes the data through three neural network branches: 1) PointNet with 3D points, exploiting the 3D information; 2) CNN with a 2D point density image in a top-down view, enhancing the local spatial reasoning; and 3) CNN with RGB images, utilizing …
We address 2D floorplan reconstruction from 3D scans. Existing approaches typically employ heuristically designed multi-stage pipelines. Instead, we formulate floorplan reconstruction as a single-stage structured prediction task: find a variable-size set of polygons, which in turn are variable-length sequences of ordered vertices. To solve it we develop a novel Transformer architecture that generates polygons of multiple rooms in parallel, in a holistic manner without hand-crafted intermediate stages. The model features two-level queries for polygons and corners, and includes polygon matching …
Floorplan-SLAM: A Real-Time, High-Accuracy, and Long-Term Multi-Session Point-Plane SLAM for Efficient Floorplan Reconstruction
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）
PolyRoom: Room-aware Transformer for Floorplan Reconstruction
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Ocean University of China（中国海洋大学）；Wuhan University（武汉大学）
We present a novel approach to align partial 3D reconstructions which may not have substantial overlap. Using floorplan priors, our method jointly predicts a room layout and estimates the transformations from a set of partial 3D data. Unlike the existing methods relying on feature descriptors to establish correspondences, we exploit the 3D "box" structure of a typical room layout that meets the Manhattan World property. We first estimate a local layout for each partial scan separately and then combine these local layouts to form a globally aligned layout with loop closure. Without the requirem…
Simon Fraser University（西蒙弗雷泽大学）；Washington University in St. Louis（华盛顿大学圣路易斯分校）
This paper proposes a new approach for automated floorplan reconstruction from RGBD scans, a major milestone in indoor mapping research. The approach, dubbed Floor-SP, formulates a novel optimization problem, where room-wise coordinate descent sequentially solves dynamic programming to optimize the floorplan graph structure. The objective function consists of data terms guided by deep neural networks, consistency terms encouraging adjacent rooms to share corners and walls, and the model complexity term. The approach does not require corner/edge detection with thresholds, unlike most other meth…
Wuhan University of Technology（武汉理工大学）；University of Twente（代尔夫特理工大学）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；The Advanced Laser Technology Laboratory of Anhui Province（安徽省先进激光技术实验室）
FloorSAM: SAM-Guided Floorplan Reconstruction with Semantic-Geometric Fusion
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Joint Laboratory of Industrial Intelligent Technologies, Russia, China（中俄工业智能技术联合实验室）；Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所）；United Institute of Informatics Problems of the National Academy of Belarus（白俄罗斯国家科学院信息问题联合研究所）；Saint Petersburg Federal Research Center of the Russian Academy of Sciences（俄罗斯科学院圣彼得堡联邦科研中心）
Spatial audio, which focuses on immersive 3D sound rendering, is widely applied in the acoustic industry. One of the key problems of current spatial audio rendering methods is the lack of personalization based on different anatomies of individuals, which is essential to produce accurate sound source positions. In this work, we address this problem from an interdisciplinary perspective. The rendering of spatial audio is strongly correlated with the 3D shape of human bodies, particularly ears. To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with sing…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Maryland at College Park（马里兰大学帕克分校）
Reflective and textureless surfaces such as windows, mirrors, and walls can be a challenge for object and scene reconstruction. These surfaces are often poorly reconstructed and filled with depth discontinuities and holes, making it difficult to cohesively reconstruct scenes that contain these planar discontinuities. We propose Echoreconstruction, an audio-visual method that uses the reflections of sound to aid in geometry and audio reconstruction for virtual conferencing, teleimmersion, and other AR/VR experience. The mobile phone prototype emits pulsed audio, while recording video for RGB-ba…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Maryland at College Park（马里兰大学帕克分校）
3D object reconstructions of transparent and concave structured objects, with inferred material properties, remains an open research problem for robot navigation in unstructured environments. In this paper, we propose a multimodal single- and multi-frame neural network for 3D reconstructions using audio-visual inputs. Our trained reconstruction LSTM autoencoder 3D-MOV accepts multiple inputs to account for a variety of surface types and views. Our neural network produces high-quality 3D reconstructions using voxel representation. Based on Intersection-over-Union (IoU), we evaluate against othe…
National Technical University of Athens（雅典国立技术大学）；Imperial College London（伦敦帝国理工学院）；Institute for Language and Speech Processing, Athena R.C.（雅典娜研究中心语言与语音处理研究所）；Foundation for Research & Technology - Hellas（希腊研究与技术基金会）；University of Exeter（埃克塞特大学）
The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer intera…
Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI
Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology（脑认知与类脑智能技术重点实验室）；State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）
Advanced Generative Adversarial Networks (GANs) are remarkable in generating intelligible audio from a random latent vector. In this paper, we examine the task of recovering the latent vector of both synthesized and real audio. Previous works recovered latent vectors of given audio through an auto-encoder inspired technique that trains an encoder network either in parallel with the GAN or after the generator is trained. With our approach, we train a deep residual neural network architecture to project audio synthesized by WaveGAN into the corresponding latent space with near identical reconstr…
VineetVC: Adaptive Video Conferencing Under Severe Bandwidth Constraints Using Audio-Driven Talking-Head Reconstruction
Homi Bhabha National Institute（霍米·巴巴国家研究所）；Variable Energy Cyclotron Centre（可变能量回旋加速器中心）；Gargi Memorial Institute of Technology（加吉纪念技术学院）；Jadavpur University（贾达普大学）
Imperial College London（帝国理工学院）；Samsung AI Centre（三星人工智能中心）
Speech is a means of communication which relies on both audio and visual information. The absence of one modality can often lead to confusion or misinterpretation of information. In this paper we present an end-to-end temporal model capable of directly synthesising audio from silent video, without needing to transform to-and-from intermediate features. Our proposed approach, based on GANs is capable of producing natural sounding, intelligible speech which is synchronised with the video. The performance of our model is evaluated on the GRID dataset for both speaker dependent and speaker indepen…
Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Objecteye Inc.（北京眼神科技有限公司）
SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃森哲-埃尔朗根-纽伦堡大学模式识别实验室）；Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根大学医院放射学研究所）；Institut für Informationsverarbeitung, Leibniz Universität Hannover（汉诺威莱比锡大学信息处理研究所）；Department of Radiology, Harvard Medical School and Massachusetts General Hospital（哈佛医学院放射科和麻省总医院）
CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
Goethe University of Frankfurt（法兰克福歌德大学）；MIT（麻省理工学院）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；Tuebingen AI Center/University of Tuebingen（图宾根人工智能中心/图宾根大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Speech separation aims to separate individual voice from an audio mixture of multiple simultaneous talkers. Although audio-only approaches achieve satisfactory performance, they build on a strategy to handle the predefined conditions, limiting their application in the complex auditory scene. Towards the cocktail party problem, we propose a novel audio-visual speech separation model. In our model, we use the face detector to detect the number of speakers in the scene and use visual information to avoid the permutation problem. To improve our model's generalization ability to unknown speakers, w…
