arXiv subjects

Chaofan Wu

Publications and source records attributed to Chaofan Wu.

4 recordsLinked to original sources

arXiv2025

Omnidirectional Depth-Aided Occupancy Prediction based on Cylindrical Voxel for Autonomous Driving

Accurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sketch-Coloring framework OmniDepth-Occ. Additionally, our approach introduces a cylindrical voxel representation based on polar coordinate to better align with the radial nature of panoramic camera views. To address the lack of fisheye camera dataset in autonomous driving tasks, we also build a virtual scene dataset with six fisheye cameras, and the data volume has reached twice that of SemanticKITTI. Experimental results demonstrate that our Sketch-Coloring network significantly enhances 3D perception performance.

cs.CV↗

arXiv2024

Real-time Multi-view Omnidirectional Depth Estimation for Real Scenarios based on Teacher-Student Learning with Unlabeled Data

Omnidirectional depth estimation enables efficient 3D perception over a full 360-degree range. However, in real-world applications such as autonomous driving and robotics, achieving real-time performance and robust cross-scene generalization remains a significant challenge for existing algorithms. In this paper, we propose a real-time omnidirectional depth estimation method for edge computing platforms named Rt-OmniMVS, which introduces the Combined Spherical Sweeping method and implements the lightweight network structure to achieve real-time performance on edge computing platforms. To achieve high accuracy, robustness, and generalization in real-world environments, we introduce a teacher-student learning strategy. We leverage the high-precision stereo matching method as the teacher model to predict pseudo labels for unlabeled real-world data, and utilize data and model augmentation techniques for training to enhance performance of the student model Rt-OmniMVS. We also propose HexaMODE, an omnidirectional depth sensing system based on multi-view fisheye cameras and edge computation device. A large-scale hybrid dataset contains both unlabeled real-world data and synthetic data is collected for model training. Experiments on public datasets demonstrate that proposed method achieves results comparable to state-of-the-art approaches while consuming significantly less resource. The proposed system and algorithm also demonstrate high accuracy in various complex real-world scenarios, both indoors and outdoors, achieving an inference speed of 15 frames per second on edge computing platforms.

cs.CV↗

arXiv2024

Dual Inflation and Bounce Cosmologies Interpretation of Pulsar Timing Array Data

We explore a dual scenario of generalized inflation and bounce cosmologies, producing a scale-invariant curvature perturbation spectrum. Bayesian analysis with pulsar timing array data identifies, for the first time, viable regions from inflation and bounce that simultaneously explain stochastic gravitational wave background (SGWB) signals and CMB anisotropies. Bayes factor calculations strongly favor this dual scenario over conventional sources and provide initial evidence of a duality between inflation and bounce regarding SGWB, offering new insights for early universe model-building and future observations.

astro-ph.CO↗

arXiv2017

A new efficient staggered grid finite difference scheme for elastic wave equation modeling

Staggered grid finite difference scheme is widely used for the first order elastic wave equation, which constitutes the basis for least-squares reverse time migration and full waveform inversion. It is of great importance to improve the efficiency and accuracy of wave equation modeling. Usually the same staggered grid finite difference scheme is used for all the spatial derivatives in the first order elastic wave equation. In this paper, we propose a new staggered grid finite difference scheme which can improve the efficiency while preserving the same accuracy for the first order elastic wave equation simulation. It uses second order staggered grid finite difference scheme for some of the first order spatial derivatives while utilizing longer staggered grid finite difference operator for other first order spatial derivatives. We The staggered grid finite difference coefficients of the new finite difference scheme are determined in the space domain by a linear method. We demonstrate by dispersion analysis and numerical simulation the effectiveness of the proposed method.

physics.geo-ph↗