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Found 6 papers, 2 papers with code
MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?"
Statler: State-Maintaining Language Models for Embodied Reasoning
In this paper, we explore a new dimension in which large language models may benefit robotics planning.
NeRFuser: Large-Scale Scene Representation by NeRF Fusion
A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small neural nets instead of collections of images.
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information.
Depth Field Networks for Generalizable Multi-view Scene Representation
Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matching.
Self-Supervised Camera Self-Calibration from Video
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams.
