BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives
Sainan Liu, Shan Lin, Jingpei Lu, Alexey Supikov, Michael Yip

TL;DR
BAA-NGP significantly accelerates neural graphics primitives for 3D scene reconstruction and pose estimation, achieving 10-20x faster results than existing methods without losing accuracy, enabling real-time robotic perception.
Contribution
The paper introduces BAA-NGP, a novel framework that uses accelerated sampling and hash encoding to speed up neural scene reconstruction and pose refinement.
Findings
Achieves 10-20x speedup over previous methods
Maintains high-quality pose estimation
Enables real-time 3D scene understanding
Abstract
Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. To successfully navigate and interact in dynamic settings, robots require the understanding of their spatial surroundings driven by unassisted reconstruction of 3D scenes and camera poses from real-time video footage. Existing approaches like COLMAP and bundle-adjusting neural radiance field methods take hours to days to process due to the high computational demands of feature matching, dense point sampling, and training of a multi-layer perceptron structure with a large number of parameters. To address these challenges, we propose a framework called bundle-adjusting accelerated neural graphics primitives (BAA-NGP) which leverages accelerated…
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Taxonomy
TopicsNeural Networks and Applications · Medical Image Segmentation Techniques · Advanced Neural Network Applications
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
