ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoder-decoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner---without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderObjectObject RecognitionSimilar Papers 제목 키워드 기반
Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting
We present EgoTAP, a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a chal…
3D Pose EstimationEgocentric Pose EstimationPose EstimationPositionC3G: Learning Compact 3D Representations with 2K Gaussians
Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. Recent approaches use per-pixel 3D Gaussian Splatting for reconstruction,…
Novel View SynthesisScene UnderstandingUnsupervised 3D Pose Estimation with Geometric Self-Supervision
We present an unsupervised learning approach to recover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi-view image data, 3D skeletons, correspondences between 2D…
3D Human Pose Estimation3D Pose EstimationPose EstimationvalidFeature-Suppressed Contrast for Self-Supervised Food Pre-training
Most previous approaches for analyzing food images have relied on extensively annotated datasets, resulting in significant human labeling expenses due to the varied and intricate nature of such images. Inspired by the ef…
Food RecognitionSelf-Supervised LearningSelf-supervised Discriminative Feature Learning for Deep Multi-view Clustering
Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views …
ClusteringDiversity