title: Neural Scene Chronology
summary: In this work, we aim to reconstruct a time-varying 3D model, capable of
  rendering photo-realistic renderings with independent control of viewpoint,
  illumination, and time, from Internet photos of large-scale landmarks. The core
  challenges are twofold. First, different types of temporal changes, such as
  illumination and changes to the underlying scene itself (such as replacing one
  graffiti artwork with another) are entangled together in the imagery. Second,
  scene-level temporal changes are often discrete and sporadic over time, rather
  than continuous. To tackle these problems, we propose a new scene
  representation equipped with a novel temporal step function encoding method
  that can model discrete scene-level content changes as piece-wise constant
  functions over time. Specifically, we represent the scene as a space-time
  radiance field with a per-image illumination embedding, where
  temporally-varying scene changes are encoded using a set of learned step
  functions. To facilitate our task of chronology reconstruction from Internet
  imagery, we also collect a new dataset of four scenes that exhibit various
  changes over time. We demonstrate that our method exhibits state-of-the-art
  view synthesis results on this dataset, while achieving independent control of
  viewpoint, time, and illumination.
paper_authors._id: 64893e904b47b34bd3d41cac
paper_authors.name: Haotong Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-14T12:15:16.943Z
paper_authors._id: 6489a01b8de3f9d810b0154f
paper_authors.avatarUrl: /avatars/f7a0fc6816535945e11bac1212dd7b57.svg
paper_authors.fullname: Haotong Lin
paper_authors.type: user
paper_authors.user: haotongl
paper_authors._id: 64893e904b47b34bd3d41cad
paper_authors.name: Qianqian Wang
paper_authors._id: 64893e904b47b34bd3d41cae
paper_authors.name: Ruojin Cai
paper_authors._id: 64893e904b47b34bd3d41caf
paper_authors.name: Sida Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T09:20:00.215Z
paper_authors._id: 62986ca2b58e71e2ac9b8f01
paper_authors.avatarUrl: /avatars/83944db5f3dbb6f47c47c46fb2cb2849.svg
paper_authors.fullname: Sida Peng
paper_authors.type: user
paper_authors.user: pengsida
paper_authors._id: 64893e904b47b34bd3d41cb0
paper_authors.name: Hadar Averbuch-Elor
paper_authors._id: 64893e904b47b34bd3d41cb1
paper_authors.name: Xiaowei Zhou
paper_authors._id: 64893e904b47b34bd3d41cb2
paper_authors.name: Noah Snavely
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T09:19:47.227Z
paper_authors._id: 632b79ca0a9dc02985af9e0b
paper_authors.avatarUrl: /avatars/e2ca585398a3212b2956a14804d0ba67.svg
paper_authors.fullname: Noah Snavely
paper_authors.type: user
paper_authors.user: jimantha
paper_title: Neural Scene Chronology
paper_summary: In this work, we aim to reconstruct a time-varying 3D model, capable of
  rendering photo-realistic renderings with independent control of viewpoint,
  illumination, and time, from Internet photos of large-scale landmarks. The core
  challenges are twofold. First, different types of temporal changes, such as
  illumination and changes to the underlying scene itself (such as replacing one
  graffiti artwork with another) are entangled together in the imagery. Second,
  scene-level temporal changes are often discrete and sporadic over time, rather
  than continuous. To tackle these problems, we propose a new scene
  representation equipped with a novel temporal step function encoding method
  that can model discrete scene-level content changes as piece-wise constant
  functions over time. Specifically, we represent the scene as a space-time
  radiance field with a per-image illumination embedding, where
  temporally-varying scene changes are encoded using a set of learned step
  functions. To facilitate our task of chronology reconstruction from Internet
  imagery, we also collect a new dataset of four scenes that exhibit various
  changes over time. We demonstrate that our method exhibits state-of-the-art
  view synthesis results on this dataset, while achieving independent control of
  viewpoint, time, and illumination.
paper_ai_summary: A novel scene representation with temporal step function encoding enables reconstruction of time-varying 3D models from internet photos, achieving state-of-the-art view synthesis with independent control over viewpoint, time, and illumination.
paper_ai_keywords: space-time radiance field illumination embedding step functions time-varying scene changes discrete content changes chronology reconstruction view synthesis