51 citations · 69 across the 36 of their papers we have counts for
18 papers · 2 filters
CALLIC: Content Adaptive Learning for Lossless Image Compression
Daxin Li, Yuanchao Bai, Kai Wang +3
Learned lossless image compression has achieved significant advancements in recent years. However, existing methods often rely on training amortized generative models on massive da…
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
Zihan Qin, Jialei Xu, Wenbo Zhao +2
Depth estimation under adverse conditions remains a significant challenge. Recently, multi-spectral depth estimation, which integrates both visible light and thermal images, has sh…
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training
Yuanqi Yao, Gang Wu, Kui Jiang +4
Learning a self-supervised Monocular Depth Estimation (MDE) model with great generalization remains significantly challenging. Despite the success of adversarial augmentation in th…
A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends
Junjun Jiang, Zengyuan Zuo, Gang Wu +2
Image restoration (IR) seeks to recover high-quality images from degraded observations caused by a wide range of factors, including noise, blur, compression, and adverse weather. W…
MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields
Yuru Xiao, Deming Zhai, Wenbo Zhao +3
Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. However, with sparse input views, the lack…
PVContext: Hybrid Context Model for Point Cloud Compression
Guoqing Zhang, Wenbo Zhao, Jian Liu +3
Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology. Recent deep learning techniques have revolutionize…
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
Xin Jin, Chunle Guo, Xiaoming Li +39
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algor…
Spatial Annealing for Efficient Few-shot Neural Rendering
Yuru Xiao, Deming Zhai, Wenbo Zhao +3
Neural Radiance Fields (NeRF) with hybrid representations have shown impressive capabilities for novel view synthesis, delivering high efficiency. Nonetheless, their performance si…
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
Yifan Mao, Ming Li, Jian Liu +7
Surround-view depth estimation is a crucial task aims to acquire the depth maps of the surrounding views. It has many applications in real world scenarios such as autonomous drivin…
Mesh Denoising Transformer
Wenbo Zhao, Xianming Liu, Deming Zhai +2
Mesh denoising, aimed at removing noise from input meshes while preserving their feature structures, is a practical yet challenging task. Despite the remarkable progress in learnin…
The Third Monocular Depth Estimation Challenge
Jaime Spencer, Fabio Tosi, Matteo Poggi +38
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-…
SDGE: Stereo Guided Depth Estimation for 360 Camera Sets
Jialei Xu, Wei Yin, Dong Gong +2
Depth estimation is a critical technology in autonomous driving, and multi-camera systems are often used to achieve a 360 perception. These 360 camera sets often ha…
SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
Yuru Xiao, Xianming Liu, Deming Zhai +3
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints. However, its effectiveness is hampered when working with sparsely available views…
Exploiting Self-Supervised Constraints in Image Super-Resolution
Gang Wu, Junjun Jiang, Kui Jiang +1
Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-…
OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
Yuchen Pan, Junjun Jiang, Kui Jiang +3
Depression Recognition (DR) poses a considerable challenge, especially in the context of the growing concerns surrounding privacy. Traditional automatic diagnosis of DR technology…
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Jialei Xu, Xianming Liu, Junjun Jiang +4
Monocular depth estimation from RGB images plays a pivotal role in 3D vision. However, its accuracy can deteriorate in challenging environments such as nighttime or adverse weather…
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning
Chenyang Wang, Junjun Jiang, Xingyu Hu +2
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, as old data from previous tasks is unavailable when learning a new task. To addre…