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description=Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.;
keywords=Nerfies, D-NeRF, NeRF;

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scene (15), the (13), and (11), language (11), for (8), this (6), set (6), text (6), our (6), #retrieval (6), are (6), open (5), natural (5), scenes (4), #graphs (4), joint (4), embedding (4), graph (4), from (3), based (3), map (3), query (3), embeddings (3), template (2), under (2), given (2), descriptions (2), method (2), potential (2), also (2), human (2), annotated (2), model (2), can (2), such (2), task (2), red (2), that (2), between (2), represented (2), match (2), not (2), they (2), correspondences (2), where (2), with (2), webpage, sincerely, thank, developing, sourcing, keunhong, park, nerfies, website, licensed, creative, commons, attribution, sharealike, international, license, showws, using, performing, evaluate, 3dssg, dataset, show, generalizability, queries, works, fashion, meaning, embed, first, step, then, retrieve, most, similar, pair, interfaces, embodied, becoming, more, ubiquitous, daily, lives, opens, further, opportunities, interaction, user, verbally, instructing, agent, execute, some, specific, location, example, put, bowls, back, cupboard, next, fridge, meet, intersection, sign, need, methods, interface, representations, environment, end, explore, question, whether, use, identify, define, closely, related, coarse, localization, but, instead, searching, collection, disjoint, necessarily, large, scale, continuous, present, text2scenegraphmatcher, pipeline, learns, determine, abstract, left, reference, environments, right, yellow, establish, matched, according, their, space, blue, these, jointly, learned, green, additionally, content, brackets, represent, downstream, applications, system, part, description, data, code, arxiv, supplementary, paper, microsoft, mixed, reality, lab, stanford, university, eth, zürich, hermann, blum, marc, pollefeys, iro, armeni, daniel, barath, jiaqi, chen,

Text of the page (random words):
where am i scene retrieval with language where am i scene retrieval with language jiaqi chen 1 daniel barath 1 iro armeni 2 marc pollefeys 1 3 hermann blum 1 1 eth zürich 2 stanford university 3 microsoft mixed reality lab paper supplementary arxiv code data given an open set natural language query left red and a reference map of environments represented by a set of 3d scene graphs right yellow we establish text to scene graph correspondences the text and scene graph correspondences are matched according to their embeddings in a joint embedding space blue these embeddings are jointly learned by a joint embedding model green additionally the text query content in brackets represent potential downstream applications for our system they are not part of the scene description abstract natural language interfaces for embodied ai are becoming more ubiquitous in our daily lives this opens up further opportunities for language based interaction such as a user verbally instructing an agent to execute some task in a specific location for example put the bowls back in the cupboard next to the fridge or meet me at the intersection under the red sign as such we need methods that interface between natural language and map representations of the environment to this end we explore the question of whether we can use an open set natural language query to identify a scene represented by a 3d scene graph we define this task as language based scene retrieval and it is closely related to coarse localization but we are instead searching for a match from a collection of disjoint scenes and not necessarily a large scale continuous map we present text2scenegraphmatcher a scene retrieval pipeline that learns joint embeddings between text descriptions and scene graphs to determine if they are a match given open set natural language text descriptions of scenes our method showws the potential for using language and scene graphs for performing scene retrieval we evaluate our method on scenes from the 3dssg dataset and also a set of human annotated scenes we show generalizability to open set human annotated language queries our joint embedding model also works in a retrieval based fashion meaning we can embed our scene graphs in a first step and then retrieve the most similar embedding text and scene graph pair this website is licensed under a creative commons attribution sharealike 4 0 international license this webpage template is from nerfies we sincerely thank keunhong park for developing and open sourcing this template