3D Audio-Visual Segmentation

• URL: http://arxiv.org/abs/2411.02236v1
• Date: Mon, 04 Nov 2024 16:30:14 GMT
• Title: 3D Audio-Visual Segmentation
• Authors: Artem Sokolov, Swapnil Bhosale, Xiatian Zhu,
• Abstract summary: Recognizing the sounding objects in scenes is a longstanding objective in embodied AI, with diverse applications in robotics and AR/VR/MR. We propose a new approach, EchoSegnet, characterized by integrating the ready-to-use knowledge from pretrained 2D audio-visual foundation models. Experiments demonstrate that EchoSegnet can effectively segment sounding objects in 3D space on our new benchmark, representing a significant advancement in the field of embodied AI.
• Score: 44.61476023587931
• License:
• Abstract: Recognizing the sounding objects in scenes is a longstanding objective in embodied AI, with diverse applications in robotics and AR/VR/MR. To that end, Audio-Visual Segmentation (AVS), taking as condition an audio signal to identify the masks of the target sounding objects in an input image with synchronous camera and microphone sensors, has been recently advanced. However, this paradigm is still insufficient for real-world operation, as the mapping from 2D images to 3D scenes is missing. To address this fundamental limitation, we introduce a novel research problem, 3D Audio-Visual Segmentation, extending the existing AVS to the 3D output space. This problem poses more challenges due to variations in camera extrinsics, audio scattering, occlusions, and diverse acoustics across sounding object categories. To facilitate this research, we create the very first simulation based benchmark, 3DAVS-S34-O7, providing photorealistic 3D scene environments with grounded spatial audio under single-instance and multi-instance settings, across 34 scenes and 7 object categories. This is made possible by re-purposing the Habitat simulator to generate comprehensive annotations of sounding object locations and corresponding 3D masks. Subsequently, we propose a new approach, EchoSegnet, characterized by integrating the ready-to-use knowledge from pretrained 2D audio-visual foundation models synergistically with 3D visual scene representation through spatial audio-aware mask alignment and refinement. Extensive experiments demonstrate that EchoSegnet can effectively segment sounding objects in 3D space on our new benchmark, representing a significant advancement in the field of embodied AI. Project page: https://surrey-uplab.github.io/research/3d-audio-visual-segmentation/

Related papers

• 3D Feature Distillation with Object-Centric Priors [9.626027459292926]
  2D vision-language models such as CLIP have been widely popularized, due to their impressive capabilities for open-vocabulary grounding in 2D images. Recent works aim to elevate 2D CLIP features to 3D via feature distillation, but either learn neural fields that are scene-specific or focus on indoor room scan data. We show that our method reconstructs 3D CLIP features with improved grounding capacity and spatial consistency.
  arXiv  Detail & Related papers  (2024-06-26T20:16:49Z)
• AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis [62.33446681243413]
  view acoustic synthesis aims to render audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition for synthesizing audio. We propose a novel Audio-Visual Gaussian Splatting (AV-GS) model to characterize the entire scene environment. Experiments validate the superiority of our AV-GS over existing alternatives on the real-world RWAS and simulation-based SoundSpaces datasets.
  arXiv  Detail & Related papers  (2024-06-13T08:34:12Z)
• Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers [65.51132104404051]
  We introduce the use of object identifiers and object-centric representations to interact with scenes at the object level. Our model significantly outperforms existing methods on benchmarks including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
  arXiv  Detail & Related papers  (2023-12-13T14:27:45Z)
• Four Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding [56.00186960144545]
  3D visual grounding is the task of localizing the object in a 3D scene which is referred by a description in natural language. We propose a dense 3D grounding network, featuring four novel stand-alone modules that aim to improve grounding performance.
  arXiv  Detail & Related papers  (2023-09-08T19:27:01Z)
• Generating Visual Spatial Description via Holistic 3D Scene Understanding [88.99773815159345]
  Visual spatial description (VSD) aims to generate texts that describe the spatial relations of the given objects within images. With an external 3D scene extractor, we obtain the 3D objects and scene features for input images. We construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.
  arXiv  Detail & Related papers  (2023-05-19T15:53:56Z)
• Audio-Visual Segmentation with Semantics [45.5917563087477]
  We propose a new problem called audio-visual segmentation (AVS) The goal is to output a pixel-level map of the object(s) that produce sound at the time of the image frame. We construct the first audio-visual segmentation benchmark, AVSBench, providing pixel-wise annotations for sounding objects in audible videos.
  arXiv  Detail & Related papers  (2023-01-30T18:53:32Z)
• Neural Groundplans: Persistent Neural Scene Representations from a Single Image [90.04272671464238]
  We present a method to map 2D image observations of a scene to a persistent 3D scene representation. We propose conditional neural groundplans as persistent and memory-efficient scene representations.
  arXiv  Detail & Related papers  (2022-07-22T17:41:24Z)
• Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction [30.951713301164016]
  Reflective and textureless surfaces such as windows, mirrors, and walls can be a challenge for object and scene reconstruction. 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.
  arXiv  Detail & Related papers  (2021-10-05T23:23:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.