Papers with Code 아카이브

Autonomous Vehicles

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>

코드

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

https://github.com/noahzn/ddnd

공식 코드

Channel-Aware Distillation Transformer for Depth Estimation on Nano Drones

https://github.com/noboevbo/ehpi_action_recognition

공식 코드

Simple yet efficient real-time pose-based action recognition

https://github.com/noboevbo/pedrec

공식 코드

PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation

https://github.com/nonmy/fdct

공식 코드

FDCT: Fast Depth Completion for Transparent Objects

https://github.com/norlab-ulaval/BorealTC

공식 코드

Proprioception Is All You Need: Terrain Classification for Boreal Forests

https://github.com/northocean/msn

공식 코드

MSN: Multi-Style Network for Trajectory Prediction

https://github.com/northsummer/lgkd

공식 코드

BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection

https://github.com/northsummer/sliceocc

공식 코드

SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation

https://github.com/notacracker/cotr

공식 코드

COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction

https://github.com/noyzzz/emap

공식 코드

Ego-Motion Aware Target Prediction Module for Robust Multi-Object Tracking

https://github.com/nranjbar/interpretable_machine_learning

공식 코드

Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME

https://github.com/nrhine1/deep_imitative_models

공식 코드

Deep Imitative Models for Flexible Inference, Planning, and Control

https://github.com/nrhine1/precog

공식 코드

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

https://github.com/nubot-nudt/bevdiffloc

공식 코드

BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model

https://github.com/nubot-nudt/elc-ois

공식 코드

ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data

https://github.com/nubot-nudt/insmos

공식 코드

InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data

https://github.com/nubot-nudt/segnet4d

공식 코드

SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud

https://github.com/nubot-nudt/ugna-vpr

공식 코드

UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

https://github.com/nullmax-vision/hip-ad

공식 코드

HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder

https://github.com/nullmax-vision/simpb

공식 코드

SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras

https://github.com/nutonomy/nuscenes-devkit

공식 코드

nuScenes: A multimodal dataset for autonomous driving

https://github.com/nutonomy/second.pytorch

공식 코드

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/nv-tlabs/cosmos-drive-dreams

공식 코드

Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

https://github.com/nv-tlabs/lift-splat-shoot

공식 코드

Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D

https://github.com/nvidia/tensorrt-llm

공식 코드

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/nvlabs/bev-planner

공식 코드

Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?

https://github.com/nvlabs/fb-bev

공식 코드

FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation

https://github.com/nvlabs/gtrs

공식 코드

Generalized Trajectory Scoring for End-to-end Multimodal Planning

https://github.com/nvlabs/hydra-mdp

공식 코드

Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation

https://github.com/nvlabs/omnidrive

공식 코드

OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

https://github.com/nvlabs/smerf

공식 코드

Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps

https://github.com/nvlabs/trajdata

공식 코드

trajdata: A Unified Interface to Multiple Human Trajectory Datasets

https://github.com/nyu-ce-projects/depth-estimation

공식 코드

Self-Supervised Learning based Depth Estimation from Monocular Images

https://github.com/o-vigia/gma3d

공식 코드

GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow

https://github.com/octoberzzzzz/ml-based-tms-cav-survey

공식 코드

Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey

https://github.com/olayasturias/monocular_visual_slam_survey

공식 코드

Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines

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