We address the estimation of the 6D pose of an unknown target spacecraft
relative to a monocular camera, a key step towards the autonomous rendezvous
and proximity operations required by future Active Debris Removal missions. We
present a novel method that enables an "off-the-shelf" spacecraft pose
estimator, which is supposed to known the target CAD model, to be applied on an
unknown target. Our method relies on an in-the wild NeRF, i.e., a Neural
Radiance Field that employs learnable appearance embeddings to represent
varying illumination conditions found in natural scenes. We train the NeRF
model using a sparse collection of images that depict the target, and in turn
generate a large dataset that is diverse both in terms of viewpoint and
illumination. This dataset is then used to train the pose estimation network.
We validate our method on the Hardware-In-the-Loop images of SPEED+ that
emulate lighting conditions close to those encountered on orbit. We demonstrate
that our method successfully enables the training of an off-the-shelf
spacecraft pose estimation network from a sparse set of images. Furthermore, we
show that a network trained using our method performs similarly to a model
trained on synthetic images generated using the CAD model of the target.

通过使用神经辐射场（NeRF）方法和离散图像集合，本研究提出了一种新的方法来在未知目标中估计航天器的 6D 姿态，实现了在未知目标上应用已知目标 CAD 模型的现有航天器姿态估计器，从而实现了自主汇合与接近操作。

利用神经辐射场进行未知空间物体姿态估计的接近操作

Leveraging Neural Radiance Fields for Pose Estimation of an Unknown  Space Object during Proximity Operations

We propose a novel network that learns a part-aligned representation for
person re-identification. It handles the body part misalignment problem, that
is, body parts are misaligned across human detections due to pose/viewpoint
change and unreliable detection. Our model consists of a two-stream network
(one stream for appearance map extraction and the other one for body part map
extraction) and a bilinear-pooling layer that generates and spatially pools a
part-aligned map. Each local feature of the part-aligned map is obtained by a
bilinear mapping of the corresponding local appearance and body part
descriptors. Our new representation leads to a robust image matching
similarity, which is equivalent to an aggregation of the local similarities of
the corresponding body parts combined with the weighted appearance similarity.
This part-aligned representation reduces the part misalignment problem
significantly. Our approach is also advantageous over other pose-guided
representations (e.g., extracting representations over the bounding box of each
body part) by learning part descriptors optimal for person re-identification.
For training the network, our approach does not require any part annotation on
the person re-identification dataset. Instead, we simply initialize the part
sub-stream using a pre-trained sub-network of an existing pose estimation
network, and train the whole network to minimize the re-identification loss. We
validate the effectiveness of our approach by demonstrating its superiority
over the state-of-the-art methods on the standard benchmark datasets, including
Market-1501, CUHK03, CUHK01 and DukeMTMC, and standard video dataset MARS.

本文提出了一种新的网络，通过两个流对人体的外观特征和身体部位特征进行提取，使用双线性池化层生成和空间池化对齐后的部分特征图，从而有效地解决了人体部位错位问题，并且在不需要对其进行任何标注的情况下对整个网络进行训练。