POC-SLT: Partial Object Completion with SDF Latent Transformers
Keywords: SDF Completion, Masked Encoder Transformer, Variation Auto Encoder, Scale Invariant, Million-scale Point Processing, Repairing Latent Codes
Abstract: 3D geometric shape completion hinges on representation learning and a deep understanding of geometric data.
Without profound insights into the three-dimensional nature of the data, this task remains unattainable.
Our work addresses this challenge of 3D shape completion given partial observations
by proposing a transformer operating on the latent space representing Signed Distance Fields (SDFs).
Instead of a monolithic volume, the SDF of an object is partitioned into smaller high-resolution patches leading to a sequence of latent codes.
The approach relies on a smooth latent space encoding learned via a variational autoencoder (VAE), trained on millions of 3D patches.
We employ an efficient masked autoencoder transformer to complete partial sequences into comprehensive shapes in latent space.
Our approach is extensively evaluated on partial observations from ShapeNet and the ABC dataset where only fractions of the objects are given.
The proposed POC-SLT architecture compares favorably with several baseline state-of-the-art methods,
demonstrating a significant improvement in 3D shape completion, both qualitatively and quantitatively.
Supplementary Material: zip
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 6219
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