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Found 13 papers, 7 papers with code
CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning
Neural Radiance Fields (NeRF) have demonstrated impressive performance in novel view synthesis.
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPUs from training or deploying LLMs.
Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity
Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable.
SphereDepth: Panorama Depth Estimation from Spherical Domain
The panorama image can simultaneously demonstrate complete information of the surrounding environment and has many advantages in virtual tourism, games, robotics, etc.
Ranked #17 on
Depth Estimation
on Stanford2D3D Panoramic
EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching
However, existing NAS studies on the dense prediction task, especially stereo matching, still cannot be efficiently and effectively deployed on devices of different computing capabilities.
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks
The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based methods.
FADNet: A Fast and Accurate Network for Disparity Estimation
Deep neural networks (DNNs) have achieved great success in the area of computer vision.
IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation
Besides, we present DTN-Net, a two-stage deep model for surface normal estimation.
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees
To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers.
Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training
Different from the existing end-to-end benchmarks which only present the training time, We try to investigate the impact of hardware, vendor's software library, and deep learning framework on the performance and energy consumption of AI training.
AutoML: A Survey of the State-of-the-Art
Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience.
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review
We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation.
A Distributed Synchronous SGD Algorithm with Global Top-$k$ Sparsification for Low Bandwidth Networks
Current methods that use AllGather to accumulate the sparse gradients have a communication complexity of $O(kP)$, where $P$ is the number of workers, which is inefficient on low bandwidth networks with a large number of workers.
