Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation
Current 3D-aware pretraining methods for embodied perception and manipulation are largely built on differentiable rendering frameworks, producing either fully implicit neural fields or fully explicit geometric primitives. Implicit representations, while expressive, lack explicit structural cues, whereas explicit ones preserve geometry but suffer from resolution limits and weak generalization. To address these limitations, we propose a novel pretraining framework that learns a hybrid representation-structural latent points. Specifically, we insert a point-wise latent variational autoencoder into the latent space of a point-cloud autoencoder, jointly regularizing point-wise features and coo...
| Authors | Yicheng Jiang, Jiaxu Wang, Junhao He et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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