GazeOnce360: Fisheye-Based 360° Multi-Person Gaze Estimation with Global-Local Feature Fusion

• URL: http://arxiv.org/abs/2603.17161v1
• Date: Tue, 17 Mar 2026 21:46:31 GMT
• Title: GazeOnce360: Fisheye-Based 360° Multi-Person Gaze Estimation with Global-Local Feature Fusion
• Abstract summary: We present GazeOnce360, a novel end-to-end model for multi-person gaze estimation from a single tabletop-mounted fisheye camera.<n>Our model tackles the severe distortion and perspective variation inherent in fisheye imagery by incorporating rotational convolutions and eye landmark supervision.<n>This work highlights the feasibility and potential of fisheye-based 360 gaze estimation in practical multi-person scenarios.
• Score: 10.694557991281549
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present GazeOnce360, a novel end-to-end model for multi-person gaze estimation from a single tabletop-mounted upward-facing fisheye camera. Unlike conventional approaches that rely on forward-facing cameras in constrained viewpoints, we address the underexplored setting of estimating the 3D gaze direction of multiple people distributed across a 360° scene from an upward fisheye perspective. To support research in this setting, we introduce MPSGaze360, a large-scale synthetic dataset rendered using Unreal Engine, featuring diverse multi-person configurations with accurate 3D gaze and eye landmark annotations. Our model tackles the severe distortion and perspective variation inherent in fisheye imagery by incorporating rotational convolutions and eye landmark supervision. To better capture fine-grained eye features crucial for gaze estimation, we propose a dual-resolution architecture that fuses global low-resolution context with high-resolution local eye regions. Experimental results demonstrate the effectiveness of each component in our model. This work highlights the feasibility and potential of fisheye-based 360° gaze estimation in practical multi-person scenarios. Project page: https://caizhuojiang.github.io/GazeOnce360/.

Related papers

• MIVIFI: Bridging Perspective and Fisheye Domains for Training Multi-View Fisheye Image Generation Models [44.93230202195852]
  Fisheye cameras offer a cost-effective solution by enabling full surround coverage with as few as two sensors.<n>Existing multi-view fisheye datasets are limited, and rare corner cases typically require computationally expensive 3D simulations.<n>We present two distinct methods for generating multi-view fisheye images conditioned on volumetric semantic representations.
  arXiv  Detail & Related papers  (2026-08-24T11:46:28Z)
• UniSHARP: Universal Sharp Monocular View Synthesis [69.33495127820123]
  We focus on extending SHARP, the popular view synthesis method, for universal monocular rendering across a continuum of camera systems.<n>To overcome the pinhole-specific assumptions of SHARP, our key idea is to align various images in a unified omnidirectional latent space.<n>We propose UniSHARP, which performs implicit alignment in both feature and Gaussian spaces.
  arXiv  Detail & Related papers  (2026-06-05T17:59:41Z)
• PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset [1.7596293300635233]
  PicoEyes is a unified gaze estimation framework that directly predicts all key attributes of gaze.<n>It simultaneously addresses calibration, gaze forecasting, and varying device postures.<n>It supports 3D eye reconstruction via joint estimation of eye parameters and depth maps in an end-to-end manner.
  arXiv  Detail & Related papers  (2026-05-08T03:34:09Z)
• Pano360: Perspective to Panoramic Vision with Geometric Consistency [7.713672589538202]
  We adopt a novel transformer-based architecture to achieve 3D awareness and aggregate global information across all views.<n>To establish an evaluation benchmark and train our network, we constructed a large-scale dataset of real-world scenes.
  arXiv  Detail & Related papers  (2026-03-12T14:56:14Z)
• OMEGA-Avatar: One-shot Modeling of 360° Gaussian Avatars [54.688420347927725]
  OMEGA-Avatar is the first framework that simultaneously generates a generalizable, 360-complete, and animatable 3D Gaussian head from a single image.<n>We show that OMEGA-Avatar achieves state-of-the-art performance, significantly outperforming existing baselines in 360 full-head completeness.
  arXiv  Detail & Related papers  (2026-02-12T08:16:38Z)
• ViewMorpher3D: A 3D-aware Diffusion Framework for Multi-Camera Novel View Synthesis in Autonomous Driving [20.935790354765604]
  We introduce ViewMorpher3D, a multi-view image enhancement framework based on image diffusion models.<n>Unlike single-view approaches, ViewMorpher3D jointly processes a set of rendered views conditioned on camera poses, 3D geometric priors, and temporally adjacent or spatially overlapping reference views.<n>Our framework accommodates variable numbers of cameras and flexible reference/target view configurations, making it adaptable to diverse sensor setups.
  arXiv  Detail & Related papers  (2026-01-12T13:44:14Z)
• PFDepth: Heterogeneous Pinhole-Fisheye Joint Depth Estimation via Distortion-aware Gaussian-Splatted Volumetric Fusion [61.6340987158734]
  We present the first pinhole-fisheye framework for heterogeneous multi-view depth estimation, PFDepth.<n> PFDepth employs a unified architecture capable of processing arbitrary combinations of pinhole and fisheye cameras with varied intrinsics and extrinsics.<n>We show that PFDepth sets a state-of-the-art performance on KITTI-360 and RealHet datasets over current mainstream depth networks.
  arXiv  Detail & Related papers  (2025-09-30T09:38:59Z)
• PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video Diffusion [87.13016347332943]
  PanoWorld-X is a novel framework for high-fidelity and controllable panoramic video generation with diverse camera trajectories.<n>Our experiments demonstrate superior performance in various aspects, including motion range, control precision, and visual quality.
  arXiv  Detail & Related papers  (2025-09-29T16:22:00Z)
• Seam360GS: Seamless 360° Gaussian Splatting from Real-World Omnidirectional Images [22.213607618728705]
  We introduce a novel calibration framework that incorporates a dual-fisheye camera model into the 3D Gaussian splatting pipeline.<n>Our approach not only simulates the realistic visual artifacts produced by dual-fisheye cameras but also enables the synthesis of seamlessly rendered 360-degree images.
  arXiv  Detail & Related papers  (2025-08-27T17:46:46Z)
• 3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views [67.00931529296788]
  We propose to train general gaze estimation models which can be directly employed in novel environments without adaptation. We create a large-scale dataset of diverse faces with gaze pseudo-annotations, which we extract based on the 3D geometry of the scene. We test our method in the task of gaze generalization, in which we demonstrate improvement of up to 30% compared to state-of-the-art when no ground truth data are available.
  arXiv  Detail & Related papers  (2022-12-06T14:15:17Z)
• BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation [59.11106101006008]
  We propose BiFuse++ to explore the combination of bi-projection fusion and the self-training scenario. We propose a new fusion module and Contrast-Aware Photometric Loss to improve the performance of BiFuse.
  arXiv  Detail & Related papers  (2022-09-07T06:24:21Z)
• SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
  We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
  arXiv  Detail & Related papers  (2022-04-07T17:58:47Z)
• 360MonoDepth: High-Resolution 360{\deg} Monocular Depth Estimation [15.65828728205071]
  monocular depth estimation remains a challenge for 360deg data. Current CNN-based methods do not support such high resolutions due to limited GPU memory. We propose a flexible framework for monocular depth estimation from high-resolution 360deg images using tangent images.
  arXiv  Detail & Related papers  (2021-11-30T18:57:29Z)

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.