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Multi-View Supervision for Single-View Reconstruction via Differentiable Ray Consistency

We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g., foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.12 Part 1 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 8754-8765

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

3D RECONSTRUCTION
MULTI-VIEW SUPERVISION
RAY CONSISTENCY

Info Detail Spesifik

10.1109/TPAMI.2019.2898859

Pernyataan Tanggungjawab

Shubham Tulsiani, Tinghui Zhou, Alexei A. Efros, Jitendra Malik

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