67 research outputs found
Unique Solution of a Coupled Fractional Differential System Involving Integral Boundary Conditions from Economic Model
We study the existence and uniqueness of the positive solution for the fractional differential system involving the Riemann-Stieltjes integral boundary conditions , , , , , and , where , , and and are the standard Riemann-Liouville derivatives, and are functions of bounded variation, and and denote the Riemann-Stieltjes integral. Our results are based on a generalized fixed point theorem for weakly contractive mappings in partially ordered sets
Deep Planar Parallax for Monocular Depth Estimation
Recent research has highlighted the utility of Planar Parallax Geometry in
monocular depth estimation. However, its potential has yet to be fully realized
because networks rely heavily on appearance for depth prediction. Our in-depth
analysis reveals that utilizing flow-pretrain can optimize the network's usage
of consecutive frame modeling, leading to substantial performance enhancement.
Additionally, we propose Planar Position Embedding (PPE) to handle dynamic
objects that defy static scene assumptions and to tackle slope variations that
are challenging to differentiate. Comprehensive experiments on autonomous
driving datasets, namely KITTI and the Waymo Open Dataset (WOD), prove that our
Planar Parallax Network (PPNet) significantly surpasses existing learning-based
methods in performance
One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer
Whole-body mesh recovery aims to estimate the 3D human body, face, and hands
parameters from a single image. It is challenging to perform this task with a
single network due to resolution issues, i.e., the face and hands are usually
located in extremely small regions. Existing works usually detect hands and
faces, enlarge their resolution to feed in a specific network to predict the
parameter, and finally fuse the results. While this copy-paste pipeline can
capture the fine-grained details of the face and hands, the connections between
different parts cannot be easily recovered in late fusion, leading to
implausible 3D rotation and unnatural pose. In this work, we propose a
one-stage pipeline for expressive whole-body mesh recovery, named OSX, without
separate networks for each part. Specifically, we design a Component Aware
Transformer (CAT) composed of a global body encoder and a local face/hand
decoder. The encoder predicts the body parameters and provides a high-quality
feature map for the decoder, which performs a feature-level upsample-crop
scheme to extract high-resolution part-specific features and adopt
keypoint-guided deformable attention to estimate hand and face precisely. The
whole pipeline is simple yet effective without any manual post-processing and
naturally avoids implausible prediction. Comprehensive experiments demonstrate
the effectiveness of OSX. Lastly, we build a large-scale Upper-Body dataset
(UBody) with high-quality 2D and 3D whole-body annotations. It contains persons
with partially visible bodies in diverse real-life scenarios to bridge the gap
between the basic task and downstream applications.Comment: Accepted to CVPR2023; Top-1 on AGORA SMPLX benchmark; Project Page:
https://osx-ubody.github.io
In-Motion Initial Alignment Method Based on Vector Observation and Truncated Vectorized K-Matrix for SINS
A Coarse Alignment Method Based on Vector Observation and Truncated Vectorized κ-matrix for Underwater Vehicle
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
Inverse rendering methods aim to estimate geometry, materials and
illumination from multi-view RGB images. In order to achieve better
decomposition, recent approaches attempt to model indirect illuminations
reflected from different materials via Spherical Gaussians (SG), which,
however, tends to blur the high-frequency reflection details. In this paper, we
propose an end-to-end inverse rendering pipeline that decomposes materials and
illumination from multi-view images, while considering near-field indirect
illumination. In a nutshell, we introduce the Monte Carlo sampling based path
tracing and cache the indirect illumination as neural radiance, enabling a
physics-faithful and easy-to-optimize inverse rendering method. To enhance
efficiency and practicality, we leverage SG to represent the smooth environment
illuminations and apply importance sampling techniques. To supervise indirect
illuminations from unobserved directions, we develop a novel radiance
consistency constraint between implicit neural radiance and path tracing
results of unobserved rays along with the joint optimization of materials and
illuminations, thus significantly improving the decomposition performance.
Extensive experiments demonstrate that our method outperforms the
state-of-the-art on multiple synthetic and real datasets, especially in terms
of inter-reflection decomposition.Comment: Accepted in CVPR 202
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