17 research outputs found
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3.
Move "Evaluation on synthetic data" out to supplementary file; 4. Divide
subsection "Evaluation on general data" to subsections "Experiment on
sequential data" and "Experiment on unordered Internet data"; 5. Change Fig.
1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some
symbols; 8. Correct some typo
A study of Symmetric and Repetitive Structures in Image-Based Modeling
Ph.DDOCTOR OF PHILOSOPH
From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm
Neural radiance fields (NeRF) have shown great success in novel view
synthesis. However, recovering high-quality details from real-world scenes is
still challenging for the existing NeRF-based approaches, due to the potential
imperfect calibration information and scene representation inaccuracy. Even
with high-quality training frames, the synthetic novel views produced by NeRF
models still suffer from notable rendering artifacts, such as noise and blur.
To address this, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm
that learns a degradation-driven inter-viewpoint mixer. Specially, we design a
NeRF-style degradation modeling approach and construct large-scale training
data, enabling the possibility of effectively removing NeRF-native rendering
artifacts for deep neural networks. Moreover, beyond the degradation removal,
we propose an inter-viewpoint aggregation framework that fuses highly related
high-quality training images, pushing the performance of cutting-edge NeRF
models to entirely new levels and producing highly photo-realistic synthetic
views. Based on this paradigm, we further present NeRFLiX++ with a stronger
two-stage NeRF degradation simulator and a faster inter-viewpoint mixer,
achieving superior performance with significantly improved computational
efficiency. Notably, NeRFLiX++ is capable of restoring photo-realistic
ultra-high-resolution outputs from noisy low-resolution NeRF-rendered views.
Extensive experiments demonstrate the excellent restoration ability of
NeRFLiX++ on various novel view synthesis benchmarks.Comment: 17 pages, 16 figures. Project Page:
https://redrock303.github.io/nerflix_plus/. arXiv admin note: text overlap
with arXiv:2303.0691