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Found 22 papers, 7 papers with code
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
This paper enables high-fidelity, transferable NeRF editing by frequency decomposition.
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes Using Pre-Trained Text-to-Image Models
In particular, the third and fourth stages are iterated, with the cuts obtained in the fourth stage encouraging non-rigid alignment in the third stage to focus on regions close to the cuts.
OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation
Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation.
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
Images from video generative models are more suitable for multi-view generation because the underlying network architecture that generates them employs a temporal module to enforce frame consistency.
Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF.
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images.
3D Former: Monocular Scene Reconstruction with 3D SDF Transformers
In this work, we propose an SDF transformer network, which replaces the role of 3D CNN for better 3D feature aggregation.
Dense RGB SLAM with Neural Implicit Maps
To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder.
${S}^{2}$Net: Accurate Panorama Depth Estimation on Spherical Surface
In this paper, we propose an end-to-end deep network for monocular panorama depth estimation on a unit spherical surface.
RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds
In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task.
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation
While recent works design increasingly complicated and powerful networks to directly regress the depth map, we take the path of CRFs optimization.
Ranked #1 on
Depth Prediction
on Matterport3D
Neural Window Fully-Connected CRFs for Monocular Depth Estimation
Estimating the accurate depth from a single image is challenging since it is inherently ambiguous and ill-posed.
RCP: Recurrent Closest Point for Point Cloud
In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task.
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation.
Stereo Matching by Self-supervision of Multiscopic Vision
Self-supervised learning for depth estimation possesses several advantages over supervised learning.
DRO: Deep Recurrent Optimizer for Video to Depth
There are increasing interests of studying the video-to-depth (V2D) problem with machine learning techniques.
Cluster Contrast for Unsupervised Person Re-Identification
Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets.
Ranked #1 on
Unsupervised Person Re-Identification
on PersonX
Self-supervised Object Tracking with Cycle-consistent Siamese Networks
Self-supervised learning for visual object tracking possesses valuable advantages compared to supervised learning, such as the non-necessity of laborious human annotations and online training.
Active Perception with A Monocular Camera for Multiscopic Vision
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation for robotic applications.
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors
In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects.
Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
Moving a human body or a large and bulky object can require the strength of whole arm manipulation (WAM).
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning.
