mlsanityData to 7 Oct 2026

3D-MVP: 3D Multiview Pretraining for Robotic Manipulation

arXiv 2406.18158 ↗First posted 26 Jun 2024cs.RO cs.CVcited by 10

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Abstract (arXiv)

Recent works have shown that visual pretraining on egocentric datasets using masked autoencoders (MAE) can improve generalization for downstream robotics tasks. However, these approaches pretrain only on 2D images, while many robotics applications require 3D scene understanding. In this work, we propose 3D-MVP, a novel approach for 3D Multi-View Pretraining using masked autoencoders. We leverage Robotic View Transformer (RVT), which uses a multi-view transformer to understand the 3D scene and predict gripper pose actions. We split RVT's multi-view transformer into visual encoder and action decoder, and pretrain its visual encoder using masked autoencoding on large-scale 3D datasets such as Objaverse. We evaluate 3D-MVP on a suite of virtual robot manipulation tasks and demonstrate improved performance over baselines. Our results suggest that 3D-aware pretraining is a promising approach to improve generalization of vision-based robotic manipulation policies. Project site: https://jasonqsy.github.io/3DMVP

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 42 of its 47 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

42 of 47 references.

References · 42

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30 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 8.

10 papers.