Search Results for author:
Found 17 papers, 8 papers with code
Deep Novel View Synthesis from Colored 3D Point Clouds
We propose a new deep neural network which takes a colored 3D point cloud of a scene, and directly synthesizes a photo-realistic image from an arbitrary viewpoint.
AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration
Extensive experiments demonstrate the efficacy of the degradation objective on state-of-the-art face restoration models.
Adversarial Purification and Fine-tuning for Robust UDC Image Restoration
This study delves into the enhancement of Under-Display Camera (UDC) image restoration models, focusing on their robustness against adversarial attacks.
Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
We propose a novel end-to-end approach that leverages the learning of dense pixel-wise flow fields in pairs of ground and satellite images to calculate the camera pose.
STDG: Semi-Teacher-Student Training Paradigram for Depth-guided One-stage Scene Graph Generation
Scene Graph Generation is a critical enabler of environmental comprehension for autonomous robotic systems.
Benchmarking Ultra-High-Definition Image Reflection Removal
To the best of our knowledge, these two datasets are the first largest-scale UHD datasets for SIRR.
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
Specifically, we introduce the emotion disentangling encoder (EDE) to disentangle the emotion and content in the speech by cross-reconstructed speech signals with different emotion labels.
Robust Single Image Reflection Removal Against Adversarial Attacks
This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks.
Ranked #2 on
Reflection Removal
on Real20
SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation
In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image.
FuRPE: Learning Full-body Reconstruction from Part Experts
These novel elements of FuRPE not only serve to further refine the model but also to reduce potential biases that may arise from inaccuracies in pseudo labels, thereby optimizing the network's training process and enhancing the robustness of the model.
GIDP: Learning a Good Initialization and Inducing Descriptor Post-enhancing for Large-scale Place Recognition
Large-scale place recognition is a fundamental but challenging task, which plays an increasingly important role in autonomous driving and robotics.
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection
The insight behind introducing MonoSIM is that we propose to simulate the feature learning behaviors of a point cloud based detector for monocular detector during the training period.
Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image
Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications.
RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place Recognition Network
Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process.
SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition
Point cloud-based large scale place recognition is fundamental for many applications like Simultaneous Localization and Mapping (SLAM).
Ranked #2 on
3D Place Recognition
on Oxford RobotCar Dataset
Incorporating Orientations into End-to-end Driving Model for Steering Control
In this paper, we present a novel end-to-end deep neural network model for autonomous driving that takes monocular image sequence as input, and directly generates the steering control angle.
End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera
In this paper, we propose a monocular camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network.
