title: ICON: Incremental CONfidence for Joint Pose and Radiance Field
    Optimization
summary: Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View
  Synthesis (NVS) given a set of 2D images. However, NeRF training requires
  accurate camera pose for each input view, typically obtained by
  Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax
  this constraint, but they still often rely on decent initial poses which they
  can refine. Here we aim at removing the requirement for pose initialization. We
  present Incremental CONfidence (ICON), an optimization procedure for training
  NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate
  initial guess for poses. Further, ICON introduces ``confidence": an adaptive
  measure of model quality used to dynamically reweight gradients. ICON relies on
  high-confidence poses to learn NeRF, and high-confidence 3D structure (as
  encoded by NeRF) to learn poses. We show that ICON, without prior pose
  initialization, achieves superior performance in both CO3D and HO3D versus
  methods which use SfM pose.
paper_authors._id: 65a8ab47a6fe31817b19f30e
paper_authors.name: Weiyao Wang
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Weiyao Wang
paper_authors.type: user
paper_authors.user: weiyaow
paper_authors._id: 65a8ab47a6fe31817b19f30f
paper_authors.name: Pierre Gleize
paper_authors.status: claimed_verified
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paper_authors.fullname: Pierre Gleize
paper_authors.type: user
paper_authors.user: pgleize
paper_authors._id: 65a8ab47a6fe31817b19f310
paper_authors.name: Hao Tang
paper_authors._id: 65a8ab47a6fe31817b19f311
paper_authors.name: Xingyu Chen
paper_authors._id: 65a8ab47a6fe31817b19f312
paper_authors.name: Kevin J Liang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/fea5047893056a8bc73882b7fa1d9b5e.svg
paper_authors.fullname: Kevin J Liang
paper_authors.type: user
paper_authors.user: kevinjliang
paper_authors._id: 65a8ab47a6fe31817b19f313
paper_authors.name: Matt Feiszli
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-23T08:09:39.484Z
paper_authors._id: 65a91cda241e1c6c489d53bf
paper_authors.avatarUrl: /avatars/eb864f738db56b7e00f40a66d2e3af6c.svg
paper_authors.fullname: Matt Feiszli
paper_authors.type: user
paper_authors.user: mattfeiszli
paper_title: ICON: Incremental CONfidence for Joint Pose and Radiance Field
    Optimization
paper_summary: Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View
  Synthesis (NVS) given a set of 2D images. However, NeRF training requires
  accurate camera pose for each input view, typically obtained by
  Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax
  this constraint, but they still often rely on decent initial poses which they
  can refine. Here we aim at removing the requirement for pose initialization. We
  present Incremental CONfidence (ICON), an optimization procedure for training
  NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate
  initial guess for poses. Further, ICON introduces ``confidence": an adaptive
  measure of model quality used to dynamically reweight gradients. ICON relies on
  high-confidence poses to learn NeRF, and high-confidence 3D structure (as
  encoded by NeRF) to learn poses. We show that ICON, without prior pose
  initialization, achieves superior performance in both CO3D and HO3D versus
  methods which use SfM pose.
paper_ai_keywords: Neural Radiance Fields (NeRF) Novel View Synthesis (NVS) Structure-from-Motion (SfM) Incremental CONfidence (ICON) bi-smooth camera motion confidence high-confidence poses high-confidence 3D structure