316 research outputs found
GenPose: Generative Category-level Object Pose Estimation via Diffusion Models
Object pose estimation plays a vital role in embodied AI and computer vision,
enabling intelligent agents to comprehend and interact with their surroundings.
Despite the practicality of category-level pose estimation, current approaches
encounter challenges with partially observed point clouds, known as the
multihypothesis issue. In this study, we propose a novel solution by reframing
categorylevel object pose estimation as conditional generative modeling,
departing from traditional point-to-point regression. Leveraging score-based
diffusion models, we estimate object poses by sampling candidates from the
diffusion model and aggregating them through a two-step process: filtering out
outliers via likelihood estimation and subsequently mean-pooling the remaining
candidates. To avoid the costly integration process when estimating the
likelihood, we introduce an alternative method that trains an energy-based
model from the original score-based model, enabling end-to-end likelihood
estimation. Our approach achieves state-of-the-art performance on the REAL275
dataset, surpassing 50% and 60% on strict 5d2cm and 5d5cm metrics,
respectively. Furthermore, our method demonstrates strong generalizability to
novel categories sharing similar symmetric properties without fine-tuning and
can readily adapt to object pose tracking tasks, yielding comparable results to
the current state-of-the-art baselines
Calcium-binding proteins as markers and functional determinants of neurons in pain networks
The thesis focuses on the anatomical and cellular distribution of three EF-hand calcium
binding proteins, secretagogin, neuronal calcium-binding protein 1 (NECAB1) and NECAB2
in dorsal root ganglia (DRGs) and spinal cord of three species, mouse, rat and human, and
their possible roles in pathophysiological pain.
In Paper I and Paper IV, we report that the expression of secretagogin is limited to a small
subpopulation of peptidergic neurons in mouse and human DRGs expressing calcitonin generelated
peptide (CGRP). Secretagogin is present both in the cell bodies in the DRGs and in
the central branches in lamina I of the dorsal horn and in peripheral branches together with
CGRP; it thus centrifugally transported. The loss of secretagogin (a knockout mouse) does
not affect the development of pain hypersensitivity after nerve injury or experimentally
induced inflammation.
In Paper II, we demonstrate a wide expression of NECAB1/2 in many cell bodies in mouse
DRGs and in cell bodies/nerve terminals with a wide distribution in different laminae in the
spinal cord. NECAB2 is expressed in excitatory neurons in the spinal cord, showing a
punctate staining and often co-localization with vesicular glutamate transporter 2 (VGLUT2)
and synaptophysin. NECAB2 in DRGs is distinctly down regulated, at both mRNA and
protein levels, by peripheral nerve injury.
In Paper III, we show a conserved excitatory property and laminar distribution of NECAB2
in mouse, rat and human spinal cord, while NECAB1 exhibits species diversity with regards
to neurochemical properties in mouse and rat spinal cord. NECAB1 is present in
oligodendrocytes surrounding axons in the white matter of the human spinal cord. We also
reveal a differential expression of NECAB2, calbindin-D28k and calretinin in ependymal
cells surrounding/within (human) the spinal central canal when comparing rodents and
human.
In Paper IV, we characterize a NECAB2 population in mouse DRGs using a new NECAB2
antibody validated with help of a Necab2 knockout mouse. These NECAB2 neurons cover
previously defined the C-low threshold mechanoreceptors (LTMRs) and Aδ D-hair LTMRs.
Genetically induced loss of NECAB2 attenuates inflammatory but not neuropathic pain. This
may, tentatively, be mediated by modulation of brain-derived neurotrophic factor (BDNF)
expressed in DRGs, and through the interaction with its receptor tyrosine receptor kinase B
(TrkB) in the spinal cord to modulate spinal glutamatergic neurotransmission
GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
The use of anthropomorphic robotic hands for assisting individuals in
situations where human hands may be unavailable or unsuitable has gained
significant importance. In this paper, we propose a novel task called
human-assisting dexterous grasping that aims to train a policy for controlling
a robotic hand's fingers to assist users in grasping objects. Unlike
conventional dexterous grasping, this task presents a more complex challenge as
the policy needs to adapt to diverse user intentions, in addition to the
object's geometry. We address this challenge by proposing an approach
consisting of two sub-modules: a hand-object-conditional grasping primitive
called Grasping Gradient Field~(GraspGF), and a history-conditional residual
policy. GraspGF learns `how' to grasp by estimating the gradient from a success
grasping example set, while the residual policy determines `when' and at what
speed the grasping action should be executed based on the trajectory history.
Experimental results demonstrate the superiority of our proposed method
compared to baselines, highlighting the user-awareness and practicality in
real-world applications. The codes and demonstrations can be viewed at
"https://sites.google.com/view/graspgf"
Score-PA: Score-based 3D Part Assembly
Autonomous 3D part assembly is a challenging task in the areas of robotics
and 3D computer vision. This task aims to assemble individual components into a
complete shape without relying on predefined instructions. In this paper, we
formulate this task from a novel generative perspective, introducing the
Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly. Knowing
that score-based methods are typically time-consuming during the inference
stage. To address this issue, we introduce a novel algorithm called the Fast
Predictor-Corrector Sampler (FPC) that accelerates the sampling process within
the framework. We employ various metrics to assess assembly quality and
diversity, and our evaluation results demonstrate that our algorithm
outperforms existing state-of-the-art approaches. We release our code at
https://github.com/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly.Comment: BMVC 202
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