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(CVPR 2024) Neural Refinement for Absolute Pose Regression with Feature Synthesis
The main features of activevisionlab/nefes are: Pose Estimation.
Open-source alternatives to activevisionlab/nefes include: cvg/hierarchical-localization — This project is a 3D visual localization framework designed to determine a camera's exact position and orientation by… colmap/colmap — COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion… realsenseai/librealsense — The Intel RealSense SDK is a software development kit providing drivers and libraries for interfacing with depth… bmild/nerf — This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of… cmu-perceptual-computing-lab/convolutional-pose-machines-release. activevisionlab/nope-nerf — (CVPR 2023) NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior.
COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion pipeline. It functions as a GPU-accelerated photogrammetry tool and multi-view stereo framework designed to produce dense 3D geometry and watertight meshes from collections of 2D images. The project distinguishes itself through hardware-accelerated feature extraction and a modular camera modeling system that supports perspective, fisheye, and equirectangular lens types. It employs vocabulary tree image retrieval to efficiently identify similar images in large datasets and provides P
This project is a 3D visual localization framework designed to determine a camera's exact position and orientation by matching 2D image features against a 3D reference model. It includes a structure-from-motion pipeline to reconstruct 3D scene geometry from unordered image sets, creating the necessary spatial maps for localization. The system employs a hierarchical coarse-to-fine localization approach. This process begins with a global-descriptor image retrieval system to identify candidate reference images from a large database and progresses through local feature matching to final 3D model
This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of two-dimensional images and camera poses. It functions as a volumetric rendering engine and scene synthesizer that optimizes neural representations of spatial volumes to generate novel views of complex 3D scenes. The system implements a coordinate encoding system that transforms spatial coordinates into high-dimensional space to capture high-frequency geometric details. It also includes a neural mesh extractor that converts trained radiance fields into triangle meshes via march
The Intel RealSense SDK is a software development kit providing drivers and libraries for interfacing with depth cameras to capture color, depth, and infrared data streams. It includes a depth camera driver for device discovery and sensor configuration, a stereo vision library for computing depth maps and aligning frames, and a 3D point cloud generator to transform depth and infrared frames into spatial representations. The SDK distinguishes itself through on-chip depth calculation and stereo calibration, using internal vision processors to reduce host CPU load. It supports hardware-level str