CUDA accelerated rasterization of gaussian splatting

This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives: https://nvlabs.github.io/instant-ngp/

[ICCV 2023] SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesis

A Modular Framework for 3D Gaussian Splatting and Beyond

[ECCV 2024 - Oral] Analytic-Splatting Anti-Aliased 3D Gaussian Splatting via Analytic Integration

Point-NeRF: Point-based Neural Radiance Fields

[ICCV 2023] Tracking Anything with Decoupled Video Segmentation

Open-Sora: Democratizing Efficient Video Production for All

A playbook for systematically maximizing the performance of deep learning models.

一本系统地教你将深度学习模型的性能最大化的战术手册。

[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

[CVPR2023] LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion

OpenMMLab's next-generation platform for general 3D object detection.

Code release for NeRF (Neural Radiance Fields)

A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

myself img operator study

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

[IROS 2020] Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation

gvnn: Geometric Vision with Neural Networks

SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization

Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.

gradslam is an open source differentiable dense SLAM library for PyTorch

A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR

Methods and Implements of Deep Clustering

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06