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평가표

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tri-ml/dd3d

공식 코드

Is Pseudo-Lidar needed for Monocular 3D Object detection?

https://github.com/tri-ml/vedet

공식 코드

Viewpoint Equivariance for Multi-View 3D Object Detection

https://github.com/trumpetertimes/sparse-generation

공식 코드

Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data Volume

https://github.com/tryhiseyyysum/menol

공식 코드

Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving

https://github.com/tshizys/CoupleNet

공식 코드

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

https://github.com/tsinghua-mars-lab/geomae

공식 코드

GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training

https://github.com/tsingqguo/ABBA

공식 코드

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks

https://github.com/tsingqguo/exposure-fusion-shadow-removal

공식 코드

Auto-Exposure Fusion for Single-Image Shadow Removal

https://github.com/tsingqguo/inpaint4shadow

공식 코드

Leveraging Inpainting for Single-Image Shadow Removal

https://github.com/tsingqguo/jadena

공식 코드

Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection

https://github.com/ttaoREtw/ImGeoNet

공식 코드

ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection

https://github.com/tteepe/EarlyBird

공식 코드

EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View

https://github.com/tteepe/tracktacular

공식 코드

Lifting Multi-View Detection and Tracking to the Bird's Eye View

https://github.com/tudelft-iv/unibev

공식 코드

UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor Modalities

https://github.com/tue-mps/refnet

공식 코드

A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data

https://github.com/tum-traffic-dataset/coopdet3d

공식 코드

TUMTraf V2X Cooperative Perception Dataset

https://github.com/tum-traffic-dataset/tum-traffic-dataset-dev-kit

공식 코드

TUMTraf V2X Cooperative Perception Dataset

https://github.com/tumftm/caliv

공식 코드

CaLiV: LiDAR-to-Vehicle Calibration of Arbitrary Sensor Setups via Object Reconstruction

https://github.com/tumftm/dpft

공식 코드

DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection

https://github.com/tumftm/radargnn

공식 코드

RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception

https://github.com/tumftm/sim2realdistributionaligneddataset

공식 코드

Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level

https://github.com/tunantu/dynamic-layer-attention

공식 코드

Strengthening Layer Interaction via Dynamic Layer Attention

https://github.com/turoad/scaloss

공식 코드

SCALoss: Side and Corner Aligned Loss for Bounding Box Regression

https://github.com/tusen-ai/mv2d

공식 코드

Object as Query: Lifting any 2D Object Detector to 3D Detection

https://github.com/tusen-ai/sst

공식 코드

Super Sparse 3D Object Detection

https://github.com/tusimple/centerformer

공식 코드

CenterFormer: Center-based Transformer for 3D Object Detection

https://github.com/tusimple/simpledet

공식 코드

SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition

https://github.com/tusimple/sst

공식 코드

Embracing Single Stride 3D Object Detector with Sparse Transformer

https://github.com/tustailab/hmpnet

공식 코드

HMPNet: A Feature Aggregation Architecture for Maritime Object Detection from a Shipborne Perspective

https://github.com/twangnh/Distilling-Object-Detectors

공식 코드

Distilling Object Detectors with Fine-grained Feature Imitation

https://github.com/twangnh/SimCal

공식 코드

The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation

https://github.com/twangnh/pnp-detr

공식 코드

PnP-DETR: Towards Efficient Visual Analysis with Transformers

https://github.com/tydpan/NorCal

공식 코드

On Model Calibration for Long-Tailed Object Detection and Instance Segmentation

https://github.com/tydpan/gpc

공식 코드

Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

https://github.com/tyomj/product_detection

공식 코드

Working with scale: 2nd place solution to Product Detection in Densely Packed Scenes [Technical Report]

https://github.com/tyshiwo/MemNet

공식 코드

MemNet: A Persistent Memory Network for Image Restoration

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.