686 research outputs found
ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces
In recent years, neural implicit surface reconstruction has emerged as a
popular paradigm for multi-view 3D reconstruction. Unlike traditional
multi-view stereo approaches, the neural implicit surface-based methods
leverage neural networks to represent 3D scenes as signed distance functions
(SDFs). However, they tend to disregard the reconstruction of individual
objects within the scene, which limits their performance and practical
applications. To address this issue, previous work ObjectSDF introduced a nice
framework of object-composition neural implicit surfaces, which utilizes 2D
instance masks to supervise individual object SDFs. In this paper, we propose a
new framework called ObjectSDF++ to overcome the limitations of ObjectSDF.
First, in contrast to ObjectSDF whose performance is primarily restricted by
its converted semantic field, the core component of our model is an
occlusion-aware object opacity rendering formulation that directly
volume-renders object opacity to be supervised with instance masks. Second, we
design a novel regularization term for object distinction, which can
effectively mitigate the issue that ObjectSDF may result in unexpected
reconstruction in invisible regions due to the lack of constraint to prevent
collisions. Our extensive experiments demonstrate that our novel framework not
only produces superior object reconstruction results but also significantly
improves the quality of scene reconstruction. Code and more resources can be
found in \url{https://qianyiwu.github.io/objectsdf++}Comment: ICCV 2023. Project Page: https://qianyiwu.github.io/objectsdf++ Code:
https://github.com/QianyiWu/objectsdf_plu
Deep Learning-Based Human Pose Estimation: A Survey
Human pose estimation aims to locate the human body parts and build human
body representation (e.g., body skeleton) from input data such as images and
videos. It has drawn increasing attention during the past decade and has been
utilized in a wide range of applications including human-computer interaction,
motion analysis, augmented reality, and virtual reality. Although the recently
developed deep learning-based solutions have achieved high performance in human
pose estimation, there still remain challenges due to insufficient training
data, depth ambiguities, and occlusion. The goal of this survey paper is to
provide a comprehensive review of recent deep learning-based solutions for both
2D and 3D pose estimation via a systematic analysis and comparison of these
solutions based on their input data and inference procedures. More than 240
research papers since 2014 are covered in this survey. Furthermore, 2D and 3D
human pose estimation datasets and evaluation metrics are included.
Quantitative performance comparisons of the reviewed methods on popular
datasets are summarized and discussed. Finally, the challenges involved,
applications, and future research directions are concluded. We also provide a
regularly updated project page: \url{https://github.com/zczcwh/DL-HPE
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