Below is an abstract from a computer science research paper. Given this abstract, your task is to extract all unique relationships between entities of the following types:

- "USED-FOR": For instance, "Our method models user proficiency" -> ["method", "user proficiency", "USED-FOR"].
- "EVALUATE-FOR": For instance, "We evaluate our method using F1-measure" -> ["F1-measure", "method", "EVALUATE-FOR"].
- "FEATURE-OF": For instance, "prior knowledge of the model" -> ["prior knowledge", "model", "FEATURE-OF"].
- "HYPONYM-OF": For instance, "TUIT is a software library" -> ["TUIT", "software library", "HYPONYM-OF"].
- "PART-OF": For instance, "We incorporate NLU module into the system." -> ["NLU module", "system", "PART-OF"].
- "COMPARE": For instance, "Unlike the quantitative prior, the qualitative prior is often ignored" -> ["quantitative prior", "qualitative prior", "COMPARE"].
- "CONJUNCTION": For instance: "obtained from human expert or knowledge base" -> ["human expert", "knowledge base", "CONJUNCTION"].

Format your output as a json array. Each entry in the array should express a single relation, formatted as ["<Entity_A>", "<Entity_B>", "<RELATION_A_B>"]. An example output might look like:

[["neural networks", "classification", "USED-FOR"], ["neuron", "neural network", "PART-OF"]].

If no relations are found, please return an empty array [].

Abstract:

A 'graphics for vision' approach is proposed to address the problem of reconstruction from a large and imperfect data set: reconstruction on demand by tensor voting, or ROD-TV. ROD-TV simultaneously delivers good efficiency and robust-ness, by adapting to a continuum of primitive connectivity, view dependence, and levels of detail (LOD). Locally inferred surface elements are robust to noise and better capture local shapes. By inferring per-vertex normals at sub-voxel precision on the fly, we can achieve interpolative shading. Since these missing details can be recovered at the current level of detail, our result is not upper bounded by the scanning resolution. By relaxing the mesh connectivity requirement, we extend ROD-TV and propose a simple but effective multiscale feature extraction algorithm. ROD-TV consists of a hierarchical data structure that encodes different levels of detail. The local reconstruction algorithm is tensor voting. It is applied on demand to the visible subset of data at a desired level of detail, by traversing the data hierarchy and collecting tensorial support in a neighborhood. We compare our approach and present encouraging results.
[["sub-voxel precision", "per-vertex normals", "FEATURE-OF"], ["large and imperfect data set", "reconstruction", "USED-FOR"], ["hierarchical data structure", "ROD-TV", "PART-OF"], ["traversing the data hierarchy", "collecting tensorial support", "CONJUNCTION"], ["collecting tensorial support", "tensor voting", "USED-FOR"], ["tensor voting", "reconstruction", "USED-FOR"], ["tensor voting", "ROD-TV", "CONJUNCTION"], ["ROD-TV", "reconstruction", "USED-FOR"], ["tensor voting", "local reconstruction algorithm", "HYPONYM-OF"], ["view dependence", "primitive connectivity", "CONJUNCTION"], ["efficiency", "ROD-TV", "EVALUATE-FOR"], ["robust-ness", "efficiency", "CONJUNCTION"], ["traversing the data hierarchy", "tensor voting", "USED-FOR"], ["per-vertex normals", "interpolative shading", "USED-FOR"], ["robust-ness", "ROD-TV", "EVALUATE-FOR"], ["\u0027graphics for vision\u0027 approach", "reconstruction", "USED-FOR"], ["Locally inferred surface elements", "local shapes", "USED-FOR"], ["mesh connectivity requirement", "multiscale feature extraction algorithm", "USED-FOR"], ["ROD-TV", "multiscale feature extraction algorithm", "USED-FOR"], ["levels of detail (LOD)", "view dependence", "CONJUNCTION"]]