357 research outputs found
Cross-source Point Cloud Registration: Challenges, Progress and Prospects
The emerging topic of cross-source point cloud (CSPC) registration has
attracted increasing attention with the fast development background of 3D
sensor technologies. Different from the conventional same-source point clouds
that focus on data from same kind of 3D sensor (e.g., Kinect), CSPCs come from
different kinds of 3D sensors (e.g., Kinect and { LiDAR}). CSPC registration
generalizes the requirement of data acquisition from same-source to different
sources, which leads to generalized applications and combines the advantages of
multiple sensors. In this paper, we provide a systematic review on CSPC
registration. We first present the characteristics of CSPC, and then summarize
the key challenges in this research area, followed by the corresponding
research progress consisting of the most recent and representative developments
on this topic. Finally, we discuss the important research directions in this
vibrant area and explain the role in several application fields.Comment: Accepted by Neurocomputing 202
DiffRoom: Diffusion-based High-Quality 3D Room Reconstruction and Generation
We present DiffRoom, a novel framework for tackling the problem of
high-quality 3D indoor room reconstruction and generation, both of which are
challenging due to the complexity and diversity of the room geometry. Although
diffusion-based generative models have previously demonstrated impressive
performance in image generation and object-level 3D generation, they have not
yet been applied to room-level 3D generation due to their computationally
intensive costs. In DiffRoom, we propose a sparse 3D diffusion network that is
efficient and possesses strong generative performance for Truncated Signed
Distance Field (TSDF), based on a rough occupancy prior. Inspired by
KinectFusion's incremental alignment and fusion of local SDFs, we propose a
diffusion-based TSDF fusion approach that iteratively diffuses and fuses TSDFs,
facilitating the reconstruction and generation of an entire room environment.
Additionally, to ease training, we introduce a curriculum diffusion learning
paradigm that speeds up the training convergence process and enables
high-quality reconstruction. According to the user study, the mesh quality
generated by our DiffRoom can even outperform the ground truth mesh provided by
ScanNet
PATS: Patch Area Transportation with Subdivision for Local Feature Matching
Local feature matching aims at establishing sparse correspondences between a
pair of images. Recently, detectorfree methods present generally better
performance but are not satisfactory in image pairs with large scale
differences. In this paper, we propose Patch Area Transportation with
Subdivision (PATS) to tackle this issue. Instead of building an expensive image
pyramid, we start by splitting the original image pair into equal-sized patches
and gradually resizing and subdividing them into smaller patches with the same
scale. However, estimating scale differences between these patches is
non-trivial since the scale differences are determined by both relative camera
poses and scene structures, and thus spatially varying over image pairs.
Moreover, it is hard to obtain the ground truth for real scenes. To this end,
we propose patch area transportation, which enables learning scale differences
in a self-supervised manner. In contrast to bipartite graph matching, which
only handles one-to-one matching, our patch area transportation can deal with
many-to-many relationships. PATS improves both matching accuracy and coverage,
and shows superior performance in downstream tasks, such as relative pose
estimation, visual localization, and optical flow estimation. The source code
will be released to benefit the community.Comment: Project page: https://zju3dv.github.io/pat
RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments
It is typically challenging for visual or visual-inertial odometry systems to
handle the problems of dynamic scenes and pure rotation. In this work, we
design a novel visual-inertial odometry (VIO) system called RD-VIO to handle
both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which
can robustly detect and match keypoints in a two-stage process. In the first
state, landmarks are matched with new keypoints using visual and IMU
measurements. We collect statistical information from the matching and then
guide the intra-keypoint matching in the second stage. Secondly, to handle the
problem of pure rotation, we detect the motion type and adapt the
deferred-triangulation technique during the data-association process. We make
the pure-rotational frames into the special subframes. When solving the
visual-inertial bundle adjustment, they provide additional constraints to the
pure-rotational motion. We evaluate the proposed VIO system on public datasets.
Experiments show the proposed RD-VIO has obvious advantages over other methods
in dynamic environments
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Highly Upregulated Lhx2 in the Foxn1 Nude Mouse Phenotype Reflects a Dysregulated and Expanded Epidermal Stem Cell Niche
Hair cycling is a prime example of stem cell dependent tissue regeneration and replenishment, and its regulatory mechanisms remain poorly understood. In the present study, we evaluated the effect of a blockage in terminal keratinocytic lineage differentiation in the Foxn1 nude phenotype on the epithelial progeny. Most notably we found a constitutive upregulation of LIM homeobox protein 2 (Lhx2), a marker gene of epithelial stem cellness indispensible for hair cycle progression. However, histological evidence along with an erratic, acyclic rise of otherwise suppressed CyclinD1 levels along with several key markers of keratinocyte lineage differentiation indicate a frustrated expansion of epithelial stem cell niches in skin. In addition, CD49f/CD34/CD200–based profiling demonstrated highly significant shifts in subpopulations of epithelial progeny. Intriguingly this appeared to include the expansion of Oct4+ stem cells in dermal fractions of skin isolates in the Foxn1 knock-out opposed to wild type. Overall our findings indicate that the Foxn1 phenotype has a strong impact on epithelial progeny and thus offers a promising model to study maintenance and regulation of stem cell niches within skin not feasible in other in vitro or in vivo models
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation
Event cameras provide high temporal precision, low data rates, and high
dynamic range visual perception, which are well-suited for optical flow
estimation. While data-driven optical flow estimation has obtained great
success in RGB cameras, its generalization performance is seriously hindered in
event cameras mainly due to the limited and biased training data. In this
paper, we present a novel simulator, BlinkSim, for the fast generation of
large-scale data for event-based optical flow. BlinkSim consists of a
configurable rendering engine and a flexible engine for event data simulation.
By leveraging the wealth of current 3D assets, the rendering engine enables us
to automatically build up thousands of scenes with different objects, textures,
and motion patterns and render very high-frequency images for realistic event
data simulation. Based on BlinkSim, we construct a large training dataset and
evaluation benchmark BlinkFlow that contains sufficient, diversiform, and
challenging event data with optical flow ground truth. Experiments show that
BlinkFlow improves the generalization performance of state-of-the-art methods
by more than 40% on average and up to 90%. Moreover, we further propose an
Event optical Flow transFormer (E-FlowFormer) architecture. Powered by our
BlinkFlow, E-FlowFormer outperforms the SOTA methods by up to 91% on MVSEC
dataset and 14% on DSEC dataset and presents the best generalization
performance
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