128 research outputs found
Photonic Aharonov–Bohm effect in photon–phonon interactions
The Aharonov–Bohm effect is one of the most intriguing phenomena in both classical and
quantum physics, and associates with a number of important and fundamental issues in
quantum mechanics. The Aharonov–Bohm effects of charged particles have been experimentally
demonstrated and found applications in various fields. Recently, attention has also
focused on the Aharonov–Bohm effect for neutral particles, such as photons. Here we propose
to utilize the photon–phonon interactions to demonstrate that photonic Aharonov–Bohm
effects do exist for photons. By introducing nonreciprocal phases for photons, we observe
experimentally a gauge potential for photons in the visible range based on the photon–
phonon interactions in acousto-optic crystals, and demonstrate the photonic Aharonov–Bohm
effect. The results presented here point to new possibilities to control and manipulate
photons by designing an effective gauge potential
Ray-ONet: efficient 3D reconstruction from a single RGB image
We propose Ray-ONet to reconstruct detailed 3D models from monocular images efficiently. By predicting a series of occupancy probabilities along a ray that is back-projected from a pixel in the camera coordinate, our method Ray-ONet improves the reconstruction accuracy in comparison with Occupancy Networks (ONet), while reducing the network inference complexity to O(N2). As a result, Ray-ONet achieves state-of-the-art performance on the ShapeNet benchmark with more than 20× speed-up at 1283 resolution and maintains a similar memory footprint during inference
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
BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion
Dense 3D reconstruction from a stream of depth images is the key to many
mixed reality and robotic applications. Although methods based on Truncated
Signed Distance Function (TSDF) Fusion have advanced the field over the years,
the TSDF volume representation is confronted with striking a balance between
the robustness to noisy measurements and maintaining the level of detail. We
present Bi-level Neural Volume Fusion (BNV-Fusion), which leverages recent
advances in neural implicit representations and neural rendering for dense 3D
reconstruction. In order to incrementally integrate new depth maps into a
global neural implicit representation, we propose a novel bi-level fusion
strategy that considers both efficiency and reconstruction quality by design.
We evaluate the proposed method on multiple datasets quantitatively and
qualitatively, demonstrating a significant improvement over existing methods.Comment: Accepted at CVPR 202
NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior
Training a Neural Radiance Field (NeRF) without pre-computed camera poses is
challenging. Recent advances in this direction demonstrate the possibility of
jointly optimising a NeRF and camera poses in forward-facing scenes. However,
these methods still face difficulties during dramatic camera movement. We
tackle this challenging problem by incorporating undistorted monocular depth
priors. These priors are generated by correcting scale and shift parameters
during training, with which we are then able to constrain the relative poses
between consecutive frames. This constraint is achieved using our proposed
novel loss functions. Experiments on real-world indoor and outdoor scenes show
that our method can handle challenging camera trajectories and outperforms
existing methods in terms of novel view rendering quality and pose estimation
accuracy. Our project page is https://nope-nerf.active.vision
An Accurate and Efficient Time Delay Estimation Method of Ultra-High Frequency Signals for Partial Discharge Localization in Substations
Partial discharge (PD) localization in substations based on the ultra-high frequency (UHF) method can be used to efficiently assess insulation conditions. Localization accuracy is affected by the accuracy of the time delay (TD) estimation, which is critical for PD localization in substations. A review of existing TD estimation methods indicates that there is a need to develop methods that are both accurate and computationally efficient. In this paper, a novel TD estimation method is proposed to improve both accuracy and efficiency. The TD is calculated using an improved cross-correlation algorithm based on full-wavefronts of array UHF signals, which are extracted using the minimum cumulative energy method and zero-crossing points searching methods. The cross-correlation algorithm effectively suppresses the TD error caused by differences between full-wavefronts. To verify the method, a simulated PD source test in a laboratory and a field test in a 220 kV substation were carried out. The results show that the proposed method is accurate even in case of low signal-to-noise ratio, but with greatly improved computational efficiency
MVDream: Multi-view Diffusion for 3D Generation
We propose MVDream, a multi-view diffusion model that is able to generate
geometrically consistent multi-view images from a given text prompt. By
leveraging image diffusion models pre-trained on large-scale web datasets and a
multi-view dataset rendered from 3D assets, the resulting multi-view diffusion
model can achieve both the generalizability of 2D diffusion and the consistency
of 3D data. Such a model can thus be applied as a multi-view prior for 3D
generation via Score Distillation Sampling, where it greatly improves the
stability of existing 2D-lifting methods by solving the 3D consistency problem.
Finally, we show that the multi-view diffusion model can also be fine-tuned
under a few shot setting for personalized 3D generation, i.e. DreamBooth3D
application, where the consistency can be maintained after learning the subject
identity.Comment: Our project page is https://MV-Dream.github.i
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