mlsanityData to 7 Oct 2026

Finding NeMO: A Geometry-Aware Representation of Template Views for Few-Shot Perception

arXiv 2602.04343 ↗First posted 4 Feb 2026cs.CVcited by 0

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Abstract (arXiv)

We present Neural Memory Object (NeMO), a novel object-centric representation that can be used to detect, segment and estimate the 6DoF pose of objects unseen during training using RGB images. Our method consists of an encoder that requires only a few RGB template views depicting an object to generate a sparse object-like point cloud using a learned UDF containing semantic and geometric information. Next, a decoder takes the object encoding together with a query image to generate a variety of dense predictions. Through extensive experiments, we show that our method can be used for few-shot object perception without requiring any camera-specific parameters or retraining on target data. Our proposed concept of outsourcing object information in a NeMO and using a single network for multiple perception tasks enhances interaction with novel objects, improving scalability and efficiency by enabling quick object onboarding without retraining or extensive pre-processing. We report competitive and state-of-the-art results on various datasets and perception tasks of the BOP benchmark, demonstrating the versatility of our approach. https://github.com/DLR-RM/nemo

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 51 of its 64 references that are in the map. First posted on arXiv 4 Feb 2026.

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References · 51

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38 more
  1. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
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  3. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
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