Self-Supervised Monocular 4D Scene Reconstruction for Egocentric Videos

• URL: http://arxiv.org/abs/2411.09145v3
• Date: Sun, 16 Mar 2025 15:05:12 GMT
• Title: Self-Supervised Monocular 4D Scene Reconstruction for Egocentric Videos
• Authors: Chengbo Yuan, Geng Chen, Li Yi, Yang Gao,
• Abstract summary: EgoMono4D is a novel model that unifies the estimation of multiple variables necessary for Egocentric Monocular 4D reconstruction.<n>It achieves superior performance in dense pointclouds sequence reconstruction compared to all baselines.
• Score: 25.41337525728398
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Egocentric videos provide valuable insights into human interactions with the physical world, which has sparked growing interest in the computer vision and robotics communities. A critical challenge in fully understanding the geometry and dynamics of egocentric videos is dense scene reconstruction. However, the lack of high-quality labeled datasets in this field has hindered the effectiveness of current supervised learning methods. In this work, we aim to address this issue by exploring an self-supervised dynamic scene reconstruction approach. We introduce EgoMono4D, a novel model that unifies the estimation of multiple variables necessary for Egocentric Monocular 4D reconstruction, including camera intrinsic, camera poses, and video depth, all within a fast feed-forward framework. Starting from pretrained single-frame depth and intrinsic estimation model, we extend it with camera poses estimation and align multi-frame results on large-scale unlabeled egocentric videos. We evaluate EgoMono4D in both in-domain and zero-shot generalization settings, achieving superior performance in dense pointclouds sequence reconstruction compared to all baselines. EgoMono4D represents the first attempt to apply self-supervised learning for pointclouds sequence reconstruction to the label-scarce egocentric field, enabling fast, dense, and generalizable reconstruction. The interactable visualization, code and trained models are released https://egomono4d.github.io/

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