COMPUTER VISION

Multiview Compressive Coding for 3D Reconstruction

April 05, 2023

Abstract

A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and general-purpose representations. Comparatively, 3D poses new challenges stemming from occlusions not depicted in the image. Prior works try to overcome these by inferring from multiple views or rely on scarce CAD models and category-specific priors which hinder scaling to novel settings. In this work, we explore single-view 3D reconstruction by learning generalizable representations inspired by advances in self-supervised learning. We introduce a simple framework that operates on 3D points of single objects or whole scenes coupled with category-agnostic large-scale training from diverse RGB-D videos. Our model, Multiview Compressive Coding (MCC), learns to compress the input appearance and geometry to predict the 3D structure by querying a 3D-aware decoder. MCC's generality and efficiency allow it to learn from large-scale and diverse data sources with strong generalization to novel objects imagined by DALL⋅E 2 or captured in-the-wild with an iPhone.

Download the Paper

AUTHORS

Written by

Chao-Yuan Wu

Justin Johnson

Jitendra Malik

Christoph Feichtenhofer

Georgia Gkioxari

Publisher

CVPR

Research Topics

Computer Vision

Related Publications

September 30, 2023

INTEGRITY

COMPUTER VISION

The Stable Signature: Rooting Watermarks in Latent Diffusion Models

Pierre Fernandez, Guillaume Couairon, Hervé Jegou, Matthijs Douze, Teddy Furon

September 30, 2023

September 29, 2023

COMPUTER VISION

Among Us: Adversarially Robust Collaborative Perception by Consensus

Yiming Li, Qi Fang, Jiamu Bai, Siheng Chen, Felix Xu, Chen Feng

September 29, 2023

September 27, 2023

COMPUTER VISION

Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Xiaoliang Dai, Ji Hou, Kevin Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, Matthew Yu, Abhishek Kadian, Filip Radenovic, Dhruv Mahajan, Kunpeng Li, Yue (R) Zhao, Vladan Petrovic, Mitesh Kumar Singh, Simran Motwani, Yiwen Song, Yi Wen, Roshan Sumbaly, Vignesh Ramanathan, Zijian He, Peter Vajda, Devi Parikh

September 27, 2023

September 22, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement

Shuangzhi Li, Zhijie Wang, Felix Xu, Qing Guo, Xingyu Li, Lei Ma

September 22, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.