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MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses
Yang Fu · Ishan Misra · Xiaolong Wang

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #301
Event URL: https://oasisyang.github.io/mononerf/ »

We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of depth and camera poses. MonoNeRF follows an Autoencoder-based architecture, where the encoder estimates the monocular depth and the camera pose, and the decoder constructs a Multiplane NeRF representation based on the depth encoder feature, and renders the input frames with the estimated camera. The learning is supervised by the reconstruction error. Once the model is learned, it can be applied to multiple applications including depth estimation, camera pose estimation, and single-image novel view synthesis. More qualitative results are available at: https://oasisyang.github.io/mononerf.

Author Information

Yang Fu (University of California, San Diego)
Ishan Misra (Meta AI)
Xiaolong Wang (UC San Diego)
Xiaolong Wang

Our group has a broad interest around the directions of Computer Vision, Machine Learning and Robotics. Our focus is on learning 3D and dynamics representations through videos and physical robotic interaction data. We explore various means of supervision signals from the data itself, language, and common sense knowledge. We leverage these comprehensive representations to facilitate the learning of robot skills, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. Please check out our individual research topic of Self-Supervised Learning, Video Understanding, Common Sense Reasoning, RL and Robotics, 3D Interaction, Dexterous Hand.

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