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ReferIt3D Benchmarks

referit3d.github.io · 653 words · saved by 1 readers

ReferIt3D Project Page Code Paper ReferIt3D Benchmarks Intro With the ReferIt3D benchmarks, we wish to track and report the ongoing progress in the emerging field of language-assisted understanding and learning in real-world 3D environments. To this end, we investigate the same questions present in the ReferIt3D paper and compare methods that try to identify a single 3D object among many of a real-world scene, given appropriate referential language. Specifically we consider: How well such learning methods work when the input language is Natural as produced by speaking humans referring to the object (Nr3D challenge) vs. being template-based concerning only Spatial relations among the objects of a scene (Sr3D challenge)? How such methods are affected when we vary the number of same-to-the-target-class distracting instances in the 3D scene? E.g., when handling an "Easy" case, where the system has to find the target among two armchairs vs. a "Hard" case, where it has to find it among at l

ReferIt3D Benchmarks ReferIt3D Project Page Code Paper --> --> --> --> --> --> ReferIt3D Benchmarks Intro With the ReferIt3D benchmarks, we wish to track and report the ongoing progress in the emerging field of language-assisted understanding and learning in real-world 3D environments. To this end, we investigate the same questions present in the ReferIt3D paper and compare methods that try to identify a single 3D object among many of a real-world scene , given appropriate referential language . Specifically we consider: How well such learning methods work when the input language is Natural as

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