4DPV: 4D Pet from Videos by Coarse-to-Fine Non-Rigid Radiance Fields
This enables robust 4D reconstruction of unknown objects in the wild, which is incremental but addresses a specific bottleneck in computer vision.
The paper tackles the problem of reconstructing 4D (3D plus time) deformable objects from multiple RGB videos without using pre-built templates or controlled conditions, achieving state-of-the-art results in challenging real-world scenarios.
We present a coarse-to-fine neural deformation model to simultaneously recover the camera pose and the 4D reconstruction of an unknown object from multiple RGB sequences in the wild. To that end, our approach does not consider any pre-built 3D template nor 3D training data as well as controlled illumination conditions, and can sort out the problem in a self-supervised manner. Our model exploits canonical and image-variant spaces where both coarse and fine components are considered. We introduce a neural local quadratic model with spatio-temporal consistency to encode fine details that is combined with canonical embeddings in order to establish correspondences across sequences. We thoroughly validate the method on challenging scenarios with complex and real-world deformations, providing both quantitative and qualitative evaluations, an ablation study and a comparison with respect to competing approaches. Our project is available at https://github.com/smontode24/4DPV.