https://github.com/sankin97/logonet
공식 코드LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Papers with Code 아카이브
3D Object Detection
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3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.
<span style="color:grey; opacity: 0.6">( Image credit: AVOD )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/say2l/dcdet
공식 코드https://github.com/say2l/ffam
공식 코드FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors
https://github.com/sense-x/geomim
공식 코드https://github.com/sense-x/hop
공식 코드Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction
https://github.com/senwang98/monoskd
공식 코드https://github.com/sercharles/cn-rma
공식 코드https://github.com/serenos/di-v2x
공식 코드https://github.com/shaoqing26/fusionpainting
공식 코드FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
https://github.com/sharpiless/cascadev-det
공식 코드https://github.com/shichaoy/cube_slam
공식 코드https://github.com/shngjz/wildcamera
공식 코드Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
https://github.com/sinatayebati/radial_mae
공식 코드https://github.com/skmhrk1209/VSRD
공식 코드VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection
https://github.com/sky77764/pa-aug.pytorch
공식 코드Part-Aware Data Augmentation for 3D Object Detection in Point Cloud
https://github.com/skyhehe123/msf
공식 코드MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences
https://github.com/skyhehe123/voxset
공식 코드Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds
https://github.com/smartbot-pjlab/dort
공식 코드DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking
https://github.com/smartbot-pjlab/mv-jar
공식 코드MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training
https://github.com/snu-drl/drcpo
공식 코드https://github.com/song-jingyu/lirafusion
공식 코드LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
https://github.com/spengliang/did-m3d
공식 코드DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
https://github.com/spengliang/lpcg
공식 코드https://github.com/spengliang/occupancym3d
공식 코드https://github.com/spengliang/weakm3d
공식 코드WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
https://github.com/sshaoshuai/PointCloudDet3D
공식 코드https://github.com/sshaoshuai/PointRCNN
공식 코드PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/stardist/stardist
공식 코드Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
https://github.com/stevewongv/sparse2dense
공식 코드Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
https://github.com/stiphyjay/lidar-ptq
공식 코드LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
https://github.com/sty61010/crossdtr
공식 코드CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection
https://github.com/sunh18/monosim
공식 코드https://github.com/sunjiahao1999/spformer
공식 코드https://github.com/sunshenggu/xc_eval_pcdet
공식 코드XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.