https://github.com/robot0321/DepthRegularizedGS
공식 코드Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images
Papers with Code 아카이브
Depth Estimation
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- 보존된 평가 기록
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Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/robotvisionmu/densemonoslam
공식 코드A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving
https://github.com/rshaojimmy/AAAI2020-RFMetaFAS
공식 코드https://github.com/ruili3/Know-Your-Neighbors
공식 코드Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning
https://github.com/ruili3/dynamic-multiframe-depth
공식 코드Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
https://github.com/safetylab24/FusionCVCP
공식 코드CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction
https://github.com/salesforceairesearch/taco
공식 코드TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action
https://github.com/samia067/shoerinsics
공식 코드Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos
https://github.com/sanmin0312/P2D
공식 코드Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images
https://github.com/sanmin0312/labeldistill
공식 코드LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection
https://github.com/sauradip/depthforecasting
공식 코드How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting
https://github.com/sbrunoberenguel/calibratedconvolutions
공식 코드https://github.com/sbrunoberenguel/fredsnet
공식 코드FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions
https://github.com/schelv/monodepth2
공식 코드Improving Self-Supervised Single View Depth Estimation by Masking Occlusion
https://github.com/sconlyshootery/FeatDepth
공식 코드Feature-metric Loss for Self-supervised Learning of Depth and Egomotion
https://github.com/seannz/svr
공식 코드Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
https://github.com/seanywang0408/audioear
공식 코드AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
https://github.com/searecluse/cvprw2024
공식 코드https://github.com/senzhang-github/ekf-imu-depth
공식 코드https://github.com/sfu-gruvi-3dv/deep_human
공식 코드A Neural Network for Detailed Human Depth Estimation from a Single Image
https://github.com/shacharp/Adaptive-LiDAR-Sampling
공식 코드Adaptive LiDAR Sampling and Depth Completion using Ensemble Variance
https://github.com/shadowbbbb/depthor
공식 코드DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image
https://github.com/shai128/rrc
공식 코드https://github.com/shariqfarooq123/AdaBins
공식 코드https://github.com/shariqfarooq123/localbins
공식 코드LocalBins: Improving Depth Estimation by Learning Local Distributions
https://github.com/shawLyu/HR-Depth
공식 코드HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
https://github.com/shawn615/FlipNeRF
공식 코드FlipNeRF: Flipped Reflection Rays for Few-shot Novel View Synthesis
https://github.com/shawn615/MixNeRF
공식 코드MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputs
https://github.com/shawnricecake/quart-depth
공식 코드QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge
https://github.com/sherry97/struct-preserving-cyclegan
공식 코드Structure-preserving Image Translation for Depth Estimation in Colonoscopy Video
https://github.com/shngjz/wildcamera
공식 코드Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
https://github.com/showlab/datasetdm
공식 코드DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
https://github.com/shuweishao/af-sfmlearner
공식 코드https://github.com/shuweishao/iebins
공식 코드IEBins: Iterative Elastic Bins for Monocular Depth Estimation
https://github.com/shuweishao/monodiffusion
공식 코드MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
https://github.com/shuweishao/urcdc-depth
공식 코드URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.