Search Results for author:
Found 17 papers, 4 papers with code
ConDense: Consistent 2D/3D Pre-training for Dense and Sparse Features from Multi-View Images
To advance the state of the art in the creation of 3D foundation models, this paper introduces the ConDense framework for 3D pre-training utilizing existing pre-trained 2D networks and large-scale multi-view datasets.
MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model
The input normal maps can be predicted by 2D diffusion models, significantly aiding in the guidance and refinement of the geometry's learning.
An Origami-Inspired Endoscopic Capsule with Tactile Perception for Early Tissue Anomaly Detection
However, it is challenging to detect early-stage small nodules with this method because they lack obvious color or shape features.
TensoIR: Tensorial Inverse Rendering
We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields.
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field
Under the Lagrangian view, we parameterize the scene motion by tracking the trajectory of particles on objects.
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation from 2D Supervision
We address efficient and structure-aware 3D scene representation from images.
NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction
We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.
ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation
First, we demonstrate the transferability of our method to out-of-distribution real data by using a mixed domain learning strategy.
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
However, under an unsupervised-style setting, typical training algorithms for controllable sequence generative models suffer from the training-inference mismatch, where the same sample is used as content and style input during training but unpaired samples are given during inference.
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo
We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis.
Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance
We are interested in reconstructing the mesh representation of object surfaces from point clouds.
Disentangled Image Matting
Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.
A Comprehensive Benchmark for Single Image Compression Artifacts Reduction
We present a comprehensive study and evaluation of existing single image compression artifacts removal algorithms, using a new 4K resolution benchmark including diversified foreground objects and background scenes with rich structures, called Large-scale Ideal Ultra high definition 4K (LIU4K) benchmark.
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
To the best of our knowledge, this is the first work to investigate privacy leakage in DRL settings and we show that DRL-based agents do potentially leak privacy-sensitive information from the trained policies.
Bridging the Gap Between Computational Photography and Visual Recognition
From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal
JPEG is one of the most commonly used standards among lossy image compression methods.
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model.
