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Found 118 papers, 48 papers with code
Photon-Efficient 3D Imaging with A Non-Local Neural Network
Photon-efficient imaging has enabled a number of applications relying on single-photon sensors that can capture a 3D image with as few as one photon per pixel.
Spatial Hierarchy Aware Residual Pyramid Network for Time-of-Flight Depth Denoising
In this paper, we propose a Spatial Hierarchy Aware Residual Pyramid Network, called SHARP-Net, to remove the depth noise by fully exploiting the geometry information of the scene on different scales.
Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning
However, we identify two critical challenges in this direction: (1) VLMs tend to learn shortcuts by relying heavily on history input information, achieving seemingly strong planning results without genuinely understanding the visual inputs; and (2) the chain-ofthought (COT) reasoning processes are always misaligned with the motion planning outcomes, and how to effectively leverage the complex reasoning capability to enhance planning remains largely underexplored.
Text-Queried Audio Source Separation via Hierarchical Modeling
We first perform global-semantic separation through a global semantic feature space aligned with text queries.
Event-Enhanced Blurry Video Super-Resolution
In this paper, we tackle the task of blurry video super-resolution (BVSR), aiming to generate high-resolution (HR) videos from low-resolution (LR) and blurry inputs.
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving
To further exploit the cognitive and reasoning interactions among the 5 domains for AD understanding, we start from a small-scale VLM and train the DS models on individual domain datasets (collected from 1. 4M DS QAs across public sources).
All-in-One Image Compression and Restoration
Visual images corrupted by various types and levels of degradations are commonly encountered in practical image compression.
QMamba: Post-Training Quantization for Vision State Space Models
State Space Models (SSMs), as key components of Mamaba, have gained increasing attention for vision models recently, thanks to their efficient long sequence modeling capability.
InsTex: Indoor Scenes Stylized Texture Synthesis
Generating high-quality textures for 3D scenes is crucial for applications in interior design, gaming, and augmented/virtual reality (AR/VR).
Plug-and-Play Versatile Compressed Video Enhancement
As a widely adopted technique in data transmission, video compression effectively reduces the size of files, making it possible for real-time cloud computing.
S2D-LFE: Sparse-to-Dense Light Field Event Generation
For the first time to our knowledge, S2D-LFE enables controllable novel view synthesis only from sparse-view light field event (LFE) data, and addresses three critical challenges for the LFE generation task: simplicity, controllability, and consistency.
TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction
We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-quality 3D assets.
Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction
To address this, we introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
To exploit complementary information from the auxiliary modality, we propose a Cross-Modal Selective fusion (CMS-fusion) module that selectively incorporate the frequency and spatial features from the auxiliary modality to enhance the corresponding branch of the target modality.
Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors
Non-line-of-sight (NLOS) imaging, recovering the hidden volume from indirect reflections, has attracted increasing attention due to its potential applications.
DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network
DRExplainer constructs a directed bipartite network integrating multi-omics profiles of cell lines, the chemical structure of drugs and known drug response to achieve directed prediction.
Multi-Granularity Semantic Revision for Large Language Model Distillation
At the sequence level, we propose a sequence correction and re-generation (SCRG) strategy.
LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation
Image resampling is a basic technique that is widely employed in daily applications, such as camera photo editing.
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding
In response to this challenge, CEIA learns to align event and image data as an alternative instead of directly aligning event and text data.
Mamba-based Light Field Super-Resolution with Efficient Subspace Scanning
Transformer-based methods have demonstrated impressive performance in 4D light field (LF) super-resolution by effectively modeling long-range spatial-angular correlations, but their quadratic complexity hinders the efficient processing of high resolution 4D inputs, resulting in slow inference speed and high memory cost.
Diffusion-Promoted HDR Video Reconstruction
In this paper, we propose a diffusion-promoted method for HDR video reconstruction, termed HDR-V-Diff, which incorporates a diffusion model to capture the HDR distribution.
TokenUnify: Scalable Autoregressive Visual Pre-training with Mixture Token Prediction
Autoregressive next-token prediction is a standard pretraining method for large-scale language models, but its application to vision tasks is hindered by the non-sequential nature of image data, leading to cumulative errors.
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation
In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy encoding times.
Incorporating Degradation Estimation in Light Field Spatial Super-Resolution
Recent advancements in light field super-resolution (SR) have yielded impressive results.
Multi-modal Learnable Queries for Image Aesthetics Assessment
Instead of directly extracting aesthetic features solely from the image, user comments associated with an image could potentially provide complementary knowledge that is useful for IAA.
Event-assisted Low-Light Video Object Segmentation
In the realm of video object segmentation (VOS), the challenge of operating under low-light conditions persists, resulting in notably degraded image quality and compromised accuracy when comparing query and memory frames for similarity computation.
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models.
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals.
Towards Generalizable Tumor Synthesis
Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation.
Physics-Inspired Degradation Models for Hyperspectral Image Fusion
To fill this gap, we propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI, which comprises a spatial degradation network (SpaDN) and a spectral degradation network (SpeDN).
Diffusion-based Light Field Synthesis
Light fields (LFs), conducive to comprehensive scene radiance recorded across angular dimensions, find wide applications in 3D reconstruction, virtual reality, and computational photography. However, the LF acquisition is inevitably time-consuming and resource-intensive due to the mainstream acquisition strategy involving manual capture or laborious software synthesis. Given such a challenge, we introduce LFdiff, a straightforward yet effective diffusion-based generative framework tailored for LF synthesis, which adopts only a single RGB image as input. LFdiff leverages disparity estimated by a monocular depth estimation network and incorporates two distinctive components: a novel condition scheme and a noise estimation network tailored for LF data. Specifically, we design a position-aware warping condition scheme, enhancing inter-view geometry learning via a robust conditional signal. We then propose DistgUnet, a disentanglement-based noise estimation network, to harness comprehensive LF representations. Extensive experiments demonstrate that LFdiff excels in synthesizing visually pleasing and disparity-controllable light fields with enhanced generalization capability. Additionally, comprehensive results affirm the broad applicability of the generated LF data, spanning applications like LF super-resolution and refocusing.
Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects
The core strength of our pipeline lies in its ability to generate 3D scenes that are not only visually impressive but also exhibit features like photorealism, multi-view consistency, and diversity.
Graph Relation Distillation for Efficient Biomedical Instance Segmentation
IGD constructs a graph representing instance features and relations, transferring these two types of knowledge by enforcing instance graph consistency.
Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing
In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow.
Look-Up Table Compression for Efficient Image Restoration
In this work we propose a novel LUT compression framework to achieve a better trade-off between storage size and performance for LUT-based image restoration models.
Cross-Dimension Affinity Distillation for 3D EM Neuron Segmentation
Recent advancements utilize 3D CNNs to predict a 3D affinity map with improved accuracy but suffer from two challenges: high computational cost and limited input size especially for practical deployment for large-scale EM volumes.
Learning Large-Factor EM Image Super-Resolution with Generative Priors
As the mainstream technique for capturing images of biological specimens at nanometer resolution electron microscopy (EM) is extremely time-consuming for scanning wide field-of-view (FOV) specimens.
CBQ: Cross-Block Quantization for Large Language Models
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.
ART$\boldsymbol{\cdot}$V: Auto-Regressive Text-to-Video Generation with Diffusion Models
Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications.
Neural Degradation Representation Learning for All-In-One Image Restoration
To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations.
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
GET: Group Event Transformer for Event-Based Vision
Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing attention.
Ranked #2 on
Classification
on N-CARS
EGVD: Event-Guided Video Deraining
In this paper, we approach video deraining by employing an event camera.
Image Aesthetics Assessment via Learnable Queries
Image aesthetics assessment (IAA) aims to estimate the aesthetics of images.
Domain Adaptive Synapse Detection with Weak Point Annotations
In the second stage, we improve model generalizability on target data by regenerating square masks to get high-quality pseudo labels.
Generalized Lightness Adaptation with Channel Selective Normalization
Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability.
Mutual-Guided Dynamic Network for Image Fusion
Image fusion aims to generate a high-quality image from multiple images captured under varying conditions.
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation
Instance segmentation in electron microscopy (EM) volumes is tough due to complex shapes and sparse annotations.
Feature Decoupling-Recycling Network for Fast Interactive Segmentation
First, our model decouples the learning of source image semantics from the encoding of user guidance to process two types of input domains separately.
Deep Multi-Threshold Spiking-UNet for Image Processing
Furthermore, we adopt a flow-based training method to fine-tune the converted models, reducing time steps while preserving performance.
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding.
Ranked #1 on
Color Image Denoising
on ImageNet sigma250
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation
To address these challenges, we present Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation (GTGM), a framework that extends of VLP to 3D medical images without relying on paired textual descriptions.
Toward Real-World Light Field Super-Resolution
Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e. g., bicubic downsampling) suffer from poor performance when applied to complex real-world scenarios.
A Dive into SAM Prior in Image Restoration
This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images.
Can SAM Boost Video Super-Resolution?
To use the SAM-based prior, we propose a simple yet effective module -- SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information.
Region-Aware Portrait Retouching with Sparse Interactive Guidance
Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables
However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restricts its receptive field and thus the performance.
Toward RAW Object Detection: A New Benchmark and a New Model
In many computer vision applications (e. g., robotics and autonomous driving), high dynamic range (HDR) data is necessary for object detection algorithms to handle a variety of lighting conditions, such as strong glare.
Camouflaged Instance Segmentation via Explicit De-Camouflaging
To address these challenges, we propose a novel De-camouflaging Network (DCNet) including a pixel-level camouflage decoupling module and an instance-level camouflage suppression module.
Learning Steerable Function for Efficient Image Resampling
Image resampling is a basic technique that is widely employed in daily applications.
Event-Based Blurry Frame Interpolation Under Blind Exposure
Therefore, we first propose an exposure estimation strategy guided by event streams to estimate the lost exposure prior, transforming the blind exposure problem well-posed.
Zero-Shot Dual-Lens Super-Resolution
To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair.
Style Projected Clustering for Domain Generalized Semantic Segmentation
In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation.
Adaptive Template Transformer for Mitochondria Segmentation in Electron Microscopy Images
Mitochondria, as tiny structures within the cell, are of significant importance to study cell functions for biological and clinical analysis.
Unsupervised Video Deraining with An Event Camera
Current unsupervised video deraining methods are inefficient in modeling the intricate spatio-temporal properties of rain, which leads to unsatisfactory results.
Learning Cross-Representation Affinity Consistency for Sparsely Supervised Biomedical Instance Segmentation
Sparse instance-level supervision has recently been explored to address insufficient annotation in biomedical instance segmentation, which is easier to annotate crowded instances and better preserves instance completeness for 3D volumetric datasets compared to common semi-supervision. In this paper, we propose a sparsely supervised biomedical instance segmentation framework via cross-representation affinity consistency regularization.
DualRel: Semi-Supervised Mitochondria Segmentation From a Prototype Perspective
Automatic mitochondria segmentation enjoys great popularity with the development of deep learning.
NLOST: Non-Line-of-Sight Imaging With Transformer
Specifically, after extracting the shallow features with the assistance of physics-based priors, we design two spatial-temporal self attention encoders to explore both local and global correlations within 3D NLOS data by splitting or downsampling the features into different scales, respectively.
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending
For the first time in light field SR, we propose a potent DA strategy called CutMIB to improve the performance of existing light field SR networks while keeping their structures unchanged.
Hierarchical Prompt Learning for Multi-Task Learning
Considering this, we present Hierarchical Prompt (HiPro) learning, a simple and effective method for jointly adapting a pre-trained VLM to multiple downstream tasks.
Learning Sample Relationship for Exposure Correction
Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network.
A Soma Segmentation Benchmark in Full Adult Fly Brain
Finally, we provide quantitative and qualitative benchmark comparisons on the testset to validate the superiority of the proposed method, as well as preliminary statistics of the reconstructed somas in the full adult fly brain from the biological perspective.
Depth Estimation From Indoor Panoramas With Neural Scene Representation
Depth estimation from indoor panoramas is challenging due to the equirectangular distortions of panoramas and inaccurate matching.
Progressive Spatio-Temporal Alignment for Efficient Event-Based Motion Estimation
In addition, a dynamic batch size strategy is applied to adaptively adjust the batch size so that all events in the batch are consistent with the current motion model.
Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach
To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions.
TridentSE: Guiding Speech Enhancement with 32 Global Tokens
In this paper, we present TridentSE, a novel architecture for speech enhancement, which is capable of efficiently capturing both global information and local details.
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction
Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images.
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion
In the proposed paradigm, global and local factors in speech are explicitly decomposed and separately manipulated to achieve high speaker similarity and continuous prosody.
Recurrent Dynamic Embedding for Video Object Segmentation
In this paper, we propose a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size.
Ranked #17 on
Semi-Supervised Video Object Segmentation
on MOSE
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
Interestingly, we find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features.
aiWave: Volumetric Image Compression with 3-D Trained Affine Wavelet-like Transform
Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice.
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Last, an innovative link attention module serves as the decoder to reconstruct data from the decomposed content and style, with the help of the linking keys.
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast.
Efficient Model-Driven Network for Shadow Removal
To address these issues, we first propose a new shadow illumination model for the shadow removal task.
Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging
In this paper, we propose the quantization-aware deep optics for diffractive snapshot hyperspectral imaging.
Contextual Outpainting With Object-Level Contrastive Learning
To model the contextual correlation between foreground and background contents, we incorporate an object-level contrastive loss to regularize the learning of cross-modal representations of foreground contents and the corresponding background semantic layout, facilitating accurate semantic reasoning.
Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation
Previous LiDAR scene flow estimation methods, especially recurrent neural networks, usually suffer from structure distortion in challenging cases, such as sparse reflection and motion occlusions.
Exposure Normalization and Compensation for Multiple-Exposure Correction
However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance.
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation
In this paper, we propose Neural Spectral Reconstruction (NeSR) to lift this limitation, by introducing a novel continuous spectral representation.
Attribute Artifacts Removal for Geometry-based Point Cloud Compression
In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC.
Contrastive Learning for Mitochondria Segmentation
However, due to the image degradation during the imaging process, the large variety of mitochondrial structures, as well as the presence of noise, artifacts and other sub-cellular structures, mitochondria segmentation is very challenging.
iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform
With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods.
Towards Non-Line-of-Sight Photography
Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects.
Unfolding Taylor's Approximations for Image Restoration
Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.
Light Field Super-Resolution With Zero-Shot Learning
To fill this gap, we propose a zero-shot learning framework for light field SR, which learns a mapping to super-resolve the reference view with examples extracted solely from the input low-resolution light field itself.
Space-Time Distillation for Video Super-Resolution
In this paper, we aim to improve the performance of compact VSR networks without changing their original architectures, through a knowledge distillation approach that transfers knowledge from a complicated VSR network to a compact one.
EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration
Image deblurring has seen a great improvement with the development of deep neural networks.
Unsupervised Visual Representation Learning by Tracking Patches in Video
The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame.
Advanced Deep Networks for 3D Mitochondria Instance Segmentation
Mitochondria instance segmentation from electron microscopy (EM) images has seen notable progress since the introduction of deep learning methods.
Ranked #1 on
3D Instance Segmentation
on MitoEM
Phoneme-based Distribution Regularization for Speech Enhancement
Specifically, we propose a phoneme-based distribution regularization (PbDr) for speech enhancement, which incorporates frame-wise phoneme information into speech enhancement network in a conditional manner.
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting
We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising.
Event-Based Video Reconstruction Using Transformer
Event cameras, which output events by detecting spatio-temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency.
Ranked #2 on
Event-based Object Segmentation
on DSEC-SEG
Event-based Object Segmentation
Event-Based Video Reconstruction
+1
Tracking by Instance Detection: A Meta-Learning Approach
We propose a principled three-step approach to build a high-performance tracker.
Camera Trace Erasing
To reconcile these two demands, we propose Siamese Trace Erasing (SiamTE), in which a novel hybrid loss is designed on the basis of Siamese architecture for network training.
Is There Tradeoff between Spatial and Temporal in Video Super-Resolution?
Recent advances of deep learning lead to great success of image and video super-resolution (SR) methods that are based on convolutional neural networks (CNN).
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
Contrast enhancement and noise removal are coupled problems for low-light image enhancement.
PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network
We discover that the two streams should communicate with each other, and this is crucial to phase prediction.
Sound Audio and Speech Processing
On The Classification-Distortion-Perception Tradeoff
In this paper, we extend the previous perception-distortion tradeoff to the case of classification-distortion-perception (CDP) tradeoff, where we introduced the classification error rate of the restored signal in addition to distortion and perceptual difference.
SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking
The two stages are connected in series as the input proposals of the FM stage are generated by the CM stage.
Camera Lens Super-Resolution
Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling.
Two-Stream Action Recognition-Oriented Video Super-Resolution
Tailored for two-stream action recognition networks, we propose two video SR methods for the spatial and temporal streams respectively.
CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification
An appearance network is developed to learn appearance features from the full body, horizontal and vertical body parts of pedestrians with spatial dependencies among body parts.
Deep Boosting for Image Denoising
Boosting is a classic algorithm which has been successfully applied to diverse computer vision tasks.
Snapshot Hyperspectral Light Field Imaging
This paper presents the first snapshot hyperspectral light field imager in practice.
MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
In this paper, we propose a novel multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of a similar patch group collected in an image, which has long been neglected by previous AR models.
High-Speed Hyperspectral Video Acquisition With a Dual-Camera Architecture
We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution.
Depth Acquisition from Density Modulated Binary Patterns
First, we propose an algorithm to design the patterns to carry more phase information without compromising the depth reconstruction from a single captured image as with Kinect.
