Neural Radiance Fields (NeRF) have demonstrated impressive performance in
novel view synthesis. However, NeRF and most of its variants still rely on
traditional complex pipelines to provide extrinsic and intrinsic camera
parameters, such as COLMAP. Recent works, like NeRFmm, BARF, and L2G-NeRF,
directly treat camera parameters as learnable and estimate them through
differential volume rendering. However, these methods work for forward-looking
scenes with slight motions and fail to tackle the rotation scenario in
practice. To overcome this limitation, we propose a novel \underline{c}amera
parameter \underline{f}ree neural radiance field (CF-NeRF), which incrementally
reconstructs 3D representations and recovers the camera parameters inspired by
incremental structure from motion (SfM). Given a sequence of images, CF-NeRF
estimates the camera parameters of images one by one and reconstructs the scene
through initialization, implicit localization, and implicit optimization. To
evaluate our method, we use a challenging real-world dataset NeRFBuster which
provides 12 scenes under complex trajectories. Results demonstrate that CF-NeRF
is robust to camera rotation and achieves state-of-the-art results without
providing prior information and constraints.

通过增量式运动结构 (SfM) 启发的相机参数自由神经辐射场 (CF-NeRF) 模型，逐渐重建三维表示并恢复相机参数，能够鲁棒处理相机旋转场景，并在不提供先验信息和约束的情况下实现最先进的结果。

CF-NeRF：无需相机参数的逐步学习神经辐射场

CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental  Learning

Image keypoint descriptions that are discriminative and matchable over large
changes in viewpoint are vital for 3D reconstruction. However, descriptions
output by learned descriptors are typically not robust to camera rotation.
While they can be made more robust by, e.g., data augmentation, this degrades
performance on upright images. Another approach is test-time augmentation,
which incurs a significant increase in runtime. We instead learn a linear
transform in description space that encodes rotations of the input image. We
call this linear transform a steerer since it allows us to transform the
descriptions as if the image was rotated. From representation theory we know
all possible steerers for the rotation group. Steerers can be optimized (A)
given a fixed descriptor, (B) jointly with a descriptor or (C) we can optimize
a descriptor given a fixed steerer. We perform experiments in all of these
three settings and obtain state-of-the-art results on the rotation invariant
image matching benchmarks AIMS and Roto-360. We publish code and model weights
at github.com/georg-bn/rotation-steerers.

通过学习描述符的线性变换实现对输入图像的旋转编码，从而产生具有大视角变化下鉴别性和匹配性的图像关键点描述符，且在旋转不变图像匹配基准 AIMS 和 Roto-360 上取得了最先进的结果。

Steerers：旋转等变关键点描述符的框架

Steerers: A framework for rotation equivariant keypoint descriptors

We describe a special case of structure from motion where the camera rotates
on a sphere. The camera's optical axis lies perpendicular to the sphere's
surface. In this case, the camera's pose is minimally represented by three
rotation parameters. From analysis of the epipolar geometry we derive a novel
and efficient solution for the essential matrix relating two images, requiring
only three point correspondences in the minimal case. We apply this solver in a
structure-from-motion pipeline that aggregates pairwise relations by rotation
averaging followed by bundle adjustment with an inverse depth parameterization.
Our methods enable scene modeling with an outward-facing camera and object
scanning with an inward-facing camera.

本研究针对相机在球面上旋转的特殊情况，通过分析对极几何得出只需最少三个点的对应关系即可快速求解两张图像之间本质矩阵的方法，并将其应用于场景建模和物体扫描中。