UniT: Unified Geometry Learning with Group Autoregressive Transformer
Summary
UniT is a unified feed-forward model for geometry perception using a Group Autoregressive Transformer that integrates multiple paradigms (online/offline, multi-modal, long-horizon) while maintaining metric-scale accuracy via scale-adaptive loss and queue-style KV caching. It achieves state-of-the-art performance on ten benchmarks spanning seven tasks.
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Paper page - UniT: Unified Geometry Learning with Group Autoregressive Transformer
Source: https://huggingface.co/papers/2605.21131
Abstract
UniT presents a unified feed-forward model for geometry perception using a Group Autoregressive Transformer that integrates multiple paradigms while maintaining metric-scale accuracy through scale-adaptive loss and queue-style KV caching.
Recent feed-forward models have significantly advancedgeometry perceptionfor inferringdense 3D structurefromsensor observations. However, its essential capabilities remain fragmented across multiple incompatible paradigms, includingonline perception,offline reconstruction,multi-modal integration,long-horizon scalability, andmetric-scale estimation. We present UniT, aunified modelbuilt upon a novelGroup Autoregressive Transformer, which reformulates these seemingly disparate capabilities within a single framework. The key idea is to treat groups ofsensor observationsas the basicautoregressive unitsand predict the correspondingpoint mapsin ananchor-freeandscale-adaptivemanner. More specifically, diverse view configurations in both online and offline settings are naturally unified within a single group autoregression process. By varying the group size, online mode operates over multiple autoregressive steps with single-frame groups, whereas offline mode aggregates a multi-frame group in a single forward pass. Meanwhile, aqueue-style KV cachingmechanism ensures bounded autoregressive memory over long horizons. This is enabled by reducing long-range dependencies on early frames throughanchor-freerelational modeling, thereby allowing outdated memory to be discarded on the fly. To improve metric-scale generalization across scenes, ascale-adaptive geometry lossis further introduced within this framework. It couplesrelative geometric constraintswith a partialabsolute scale term, implicitly regularizing global scale and inducing aprogressive transitionfrom scale-invariant geometry to metric-scale solutions. Together with a dedicatedmodal attention modulefor integrating auxiliary modalities, UniT achieves state-of-the-art performance in unifiedgeometry perception, as validated on ten benchmarks spanning seven representative tasks.
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