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Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild

Paper recorded by Signals 4 on 2026-09-08 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-08 on arXiv · recorded by Signals 4 on 2026-09-09

Category: cs.CV · 计算机视觉 · first seen 2026-09-09

Abstract

Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, whereas the physical world is represented in Cartesian coordinates. This cross-space representation gap complicates geometric correspondence and semantic evidence aggregation. To delve into this challenge, we introduce Spheriverse, comprising $64,400$ tempor

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#118 most recent of 237 cs.CV papers we have recorded · ↑ newer: PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millis · ↓ older: A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction
Cite this page: Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild: the #118 most recent of 237 cs.CV papers we have recorded (as of 2026-09-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/spheriverse-3d-scene-understanding-from-spherical-observations-in-the-wild.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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