Panoramic Depth Estimation

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12 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation
Sep 30, 2026Zhijie Shen, Chunyu Lin, Shuai Zheng +4Graph Neural Networks Depth Estimation
Beijing Jiaotong University · Hefei University of Technology · Shandong University
OmniDS: Dual-Stream Context Fusion for Omnidirectional Depth from Fisheye Cameras
Jul 3, 2026Chaesong Park, Jihyeon Hwang, Muyeol Sung +1Multi-View Depth Estimation Multi-View Feature Fusion
Seoul National University, Seoul, Republic of Korea
Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling
Jun 26, 2026Qinfeng Zhu, Lei FanVision Foundation Models Panoramic Depth Estimation
University of Liverpool, United Kingdom. · Xi’an Jiaotong-Liverpool University, China.
DepthMaster: Unified Monocular Depth Estimation for Perspective and Panoramic Images
Jun 10, 2026Pengfei Wang, Shihao Wang, Liyi Chen +3Metric Depth Estimation Depth Estimation
Visual Computing Lab The Hong Kong Polytechnic University
Unified Panoramic Geometry Estimation via Multi-View Foundation Models
May 25, 2026Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer +43D Foundation Models 3D ViTs
ETH Zürich · Google · Athlence Sports +1
Sphere-Depth: A Benchmark for Depth Estimation Methods with Varying Spherical Camera Orientations
Apr 25, 2026Soulayma Gazzeh, Giuseppe Mazzola, Liliana Lo Presti +1Depth Estimation Monocular Depth Estimation
Department of Engineering, University of Palermo, Palermo, Italy
VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation
Mar 19, 2026Jiayi Yuan, Haobo Jiang, De Wen Soh +1Panoramic Depth Estimation Depth Estimation
Singapore University of Technology and Design · Nanyang Technological University
Reference-Free Omnidirectional Stereo Matching via Multi-View Consistency Maximization
Mar 16, 2026Lehuai Xu, Weiming Zhang, Yang Li +2Panoramic Depth Estimation Stereo Matching
Nanjing University, Nanjing, China · Nanyang Technological University, Singapore · HKUST(GZ), Guangzhou, China
PanoDP: Learning Collision-Free Navigation with Panoramic Depth and Differentiable Physics
Mar 8, 2026Hao Zhong, Pei Chi, Jiang Zhao +4Panoramic Depth Estimation Safe Robot Navigation
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. · Beihang University, Beijing, China. · The University of Queensland, Australia.
RePer-360: Releasing Perspective Priors for 360 $^\circ$ Depth Estimation via Self-Modulation
Mar 6, 2026Cheng Guan, Chunyu Lin, Zhijie Shen +2Domain Adaptation Depth Estimation
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China. · Visual Intelligence +X International Cooperation Joint Laboratory of MOE, Beijing 100044, China.
SO3UFormer: Learning Intrinsic Spherical Features for Rotation-Robust Panoramic Dense Prediction
Feb 26, 2026Qinfeng Zhu, Yunxi Jiang, Lei FanDepth Estimation Vision Transformer
University of Liverpool, United Kingdom. · CNRS, France. · Xi’an Jiaotong-Liverpool University, China.
Depth Anything in $360^\circ$ : Towards Scale Invariance in the Wild
Dec 28, 2025Hualie Jiang, Ziyang Song, Zhiqiang Lou +2Depth Estimation Monocular Depth Estimation
Insta360 Research

Sep 30, 2026J/K move · Enter open · S save

Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation

Zhijie Shen, Chunyu Lin, Shuai Zheng, Feng Li+3

Beijing Jiaotong University · Hefei University of Technology · Shandong University

Graph Neural Networks Depth Estimation Dense Prediction Panoramic Depth Estimation

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The equirectangular projection (ERP) is widely used for panoramic depth estimation, but its spatially varying distortion makes geometry-consistent feature modeling challenging. We revisit panoramic depth estimation by decoupling contextual modeling in native spherical space from dense ERP prediction. To this end, we propose a Fibonacci Spherical Graph (FSG) as an intermediate reasoning space to lift ERP features onto quasi-uniform Fibonacci nodes on the sphere and capture local and long-range dependencies through complementary spherical neighborhoods. The resulting spherical discretization distributes graph nodes approximately uniformly over the spherical surface, reducing the over-representation of highly stretched regions during relational modeling. Operating on a compact set of Fibonacci nodes also avoids the computational burden of constructing and processing a graph at full ERP resolution. To bridge spherical reasoning and dense prediction, we propose a Spherical Context Conditioning (SCC) module that adaptively modulates dense ERP features with the enhanced spherical representation, allowing spherical context to guide pixel-aligned depth prediction. Extensive experiments on three benchmarks demonstrate that the proposed method consistently achieves superior depth accuracy over existing approaches.