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-icmr_2021_rod2021_challenge_2nd_place_solution_ustc-nelslip

A repository for ICMR2021 ROD2021 Challenge. Our team is ustc-nelslip.

2017_iv_deep_radar

Supporting material for "Deep Stochastic Radar Models," submitted to IV 2017

2d-motion-retargeting

PyTorch implementation for our paper Learning Character-Agnostic Motion for Motion Retargeting in 2D, SIGGRAPH 2019

2djoint-3dsmpl

mapping 2D person joints to 3D SMPL model

2g4-can-radar-gui

3d-coded

Pytorch Implementation for the project : 3D-CODED : 3D Correspondences by Deep Deformation"

3d-cutter

Cut a tetrahedral mesh with a triangle mesh in 3D.

3d-hourglass-network

3D HourGlass Networks for Human Pose Estimation Through Videos

3d-human-body-shape

3d-machine-learning

A resource repository for 3D machine learning

3d-pose-estimation

3d-representation-learning

A list of papers on 3D vision & learning

3d-sis

[CVPR'19] 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans

3d_smpl

Code for Paper: Self-supervised Learning of Motion Capture

3dcnn

3D convolutional neural network for video classification

3dfeatnet

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

3dlscptr

This is an official repository of Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints

3dmatch-toolbox

3DMatch - a 3D ConvNet-based local geometric descriptor for aligning 3D meshes and point clouds.

3dmppe_posenet_release

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

3dpose-demo-iitb

Demo code for the paper Structure-Aware and Temporally Coherent 3D Human Pose Estimation

3dpose_ssl

3D Human Pose Machines with Self-supervised Learning

3dv_tutorial

An Invitation to 3D Vision: A Tutorial for Everyone

a-sparse-coding-based-approach-for-class-specific-feature-selection

A novel Sparse-Coding Based Approach Feature Selection with emphasizing joint l_1,2-norm minimization and the Class-Specific Feature Selection.

a-survey-on-generative-diffusion-model

aadc2018

Code basis used by Team AlpaKa for the Audi Autonomous Driving Cup 2018

abme

Asymmetric Bilateral Motion Estimation for Video Frame Interpolation, ICCV2021

abnormal_event_detection

Abnormal Event Detection in Videos using SpatioTemporal AutoEncoder

acoustic-direction-finding-using-single-acoustic-vector-sensor-under-high-reverberation

We propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.

adalista

Ada-LISTA: Learned Solvers Adaptive to Varying Models

adaptive-regularization-neural-network

[NeurIPS'19] [PyTorch] Adaptive Regularization in NN