Learning Shape Priors for Single-View 3D Completion and Reconstruction

The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://shapehd.csail.mit.edu

A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction

Single-image 3D reconstruction is a research challenge focused on predicting 3D object shapes from single-view images. This task requires significant data acquisition to predict both visible and occluded portions of the shape. Furthermore, learning-based methods face the difficulty of creating a comprehensive training dataset for all possible classes. To this end, we propose a continual learning-based 3D reconstruction method where our goal is to design a model using Variational Priors that can still reconstruct the previously seen classes reasonably even after training on new classes. Variational Priors represent abstract shapes and combat forgetting, whereas saliency maps preserve object attributes with less memory usage. This is vital due to resource constraints in storing extensive training data. Additionally, we introduce saliency map-based experience replay to capture global and distinct object features. Thorough experiments show competitive results compared to established methods, both quantitatively and qualitatively.Comment: 15 page

One-shot Implicit Animatable Avatars with Model-based Priors

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can effortlessly estimate the body geometry and imagine full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pretrained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to generate text-conditioned unseen regions. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT has outperformed strong baseline methods of avatar creation when only a single image is available. The code is public for research purposes at https://huangyangyi.github.io/ELICIT/.Comment: To appear at ICCV 2023. Project website: https://huangyangyi.github.io/ELICIT

Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data

Inferring the stereo structure of objects in the real world is a challenging yet practical task. To equip deep models with this ability usually requires abundant 3D supervision which is hard to acquire. It is promising that we can simply benefit from synthetic data, where pairwise ground-truth is easy to access. Nevertheless, the domain gaps are nontrivial considering the variant texture, shape and context. To overcome these difficulties, we propose a Visio-Perceptual Adaptive Network for single-view 3D reconstruction, dubbed VPAN. To generalize the model towards a real scenario, we propose to fulfill several aspects: (1) Look: visually incorporate spatial structure from the single view to enhance the expressiveness of representation; (2) Cast: perceptually align the 2D image features to the 3D shape priors with cross-modal semantic contrastive mapping; (3) Mold: reconstruct stereo-shape of target by transforming embeddings into the desired manifold. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of the proposed method in learning the 3D shape manifold from synthetic data via a single-view. The proposed method outperforms state-of-the-arts on Pix3D dataset with IoU 0.292 and CD 0.108, and reaches IoU 0.329 and CD 0.104 on Pascal 3D+

Single View Reconstruction for Human Face and Motion with Priors

Single view reconstruction is fundamentally an under-constrained problem. We aim to develop new approaches to model human face and motion with model priors that restrict the space of possible solutions. First, we develop a novel approach to recover the 3D shape from a single view image under challenging conditions, such as large variations in illumination and pose. The problem is addressed by employing the techniques of non-linear manifold embedding and alignment. Specifically, the local image models for each patch of facial images and the local surface models for each patch of 3D shape are learned using a non-linear dimensionality reduction technique, and the correspondences between these local models are then learned by a manifold alignment method. Local models successfully remove the dependency of large training databases for human face modeling. By combining the local shapes, the global shape of a face can be reconstructed directly from a single linear system of equations via least square. Unfortunately, this learning-based approach cannot be successfully applied to the problem of human motion modeling due to the internal and external variations in single view video-based marker-less motion capture. Therefore, we introduce a new model-based approach for capturing human motion using a stream of depth images from a single depth sensor. While a depth sensor provides metric 3D information, using a single sensor, instead of a camera array, results in a view-dependent and incomplete measurement of object motion. We develop a novel two-stage template fitting algorithm that is invariant to subject size and view-point variations, and robust to occlusions. Starting from a known pose, our algorithm first estimates a body configuration through temporal registration, which is used to search the template motion database for a best match. The best match body configuration as well as its corresponding surface mesh model are deformed to fit the input depth map, filling in the part that is occluded from the input and compensating for differences in pose and body-size between the input image and the template. Our approach does not require any makers, user-interaction, or appearance-based tracking. Experiments show that our approaches can achieve good modeling results for human face and motion, and are capable of dealing with variety of challenges in single view reconstruction, e.g., occlusion

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. However, recent work has challenged this belief, showing that complex encoder-decoder architectures perform similarly to nearest-neighbor baselines or simple linear decoder models that exploit large amounts of per category data in standard benchmarks. On the other hand settings where 3D shape must be inferred for new categories with few examples are more natural and require models that generalize about shapes. In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization. To address deficiencies in existing approaches to this problem, we propose three approaches that efficiently integrate a class prior into a 3D reconstruction model, allowing to account for intra-class variability and imposing an implicit compositional structure that the model should learn. Experiments on the popular ShapeNet database demonstrate that our method significantly outperform existing baselines on this task in the few-shot setting

Deeply Learned Priors for Geometric Reconstruction

This thesis comprises of a body of work that investigates the use of deeply learned priors for dense geometric reconstruction of scenes. A typical image captured by a 2D camera sensor is a lossy two-dimensional (2D) projection of our three-dimensional (3D) world. Geometric reconstruction approaches usually recreate the lost structural information by taking in multiple images observing a scene from different views and solving a problem known as Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM). Remarkably, by establishing correspondences across images and use of geometric models, these methods (under reasonable conditions) can reconstruct a scene's 3D structure as well as precisely localise the observed views relative to the scene. The success of dense every-pixel multi-view reconstruction is however limited by matching ambiguities that commonly arise due to uniform texture, occlusion, and appearance distortion, among several other factors. The standard approach to deal with matching ambiguities is to handcraft priors based on assumptions like piecewise smoothness or planarity in the 3D map, in order to "fill in" map regions supported by little or ambiguous matching evidence. In this thesis we propose learned priors that in comparison more closely model the true structure of the scene and are based on geometric information predicted from the images. The motivation stems from recent advancements in deep learning algorithms and availability of massive datasets, that have allowed Convolutional Neural Networks (CNNs) to predict geometric properties of a scene such as point-wise surface normals and depths, from just a single image, more reliably than what was possible using previous machine learning-based or hand-crafted methods. In particular, we first explore how single image-based surface normals from a CNN trained on massive amount of indoor data can benefit the accuracy of dense reconstruction given input images from a moving monocular camera. Here we propose a novel surface normal based inverse depth regularizer and compare its performance against the inverse depth smoothness prior that is typically used to regularize regions in the reconstruction that are textureless. We also propose the first real-time CNN-based framework for live dense monocular reconstruction using our learned normal prior. Next, we look at how we can use deep learning to learn features in order to improve the pixel matching process itself, which is at the heart of multi-view geometric reconstruction. We propose a self-supervised feature learning scheme using RGB-D data from a 3D sensor (that does not require any manual labelling) and a multi-scale CNN architecture for feature extraction that is fast and eficient to run inside our proposed real-time monocular reconstruction framework. We extensively analyze the combined benefits of using learned normals and deep features that are good-for-matching in the context of dense reconstruction, both quantitatively and qualitatively on large real world datasets. Lastly, we explore how learned depths, also predicted on a per-pixel basis from a single image using a CNN, can be used to inpaint sparse 3D maps obtained from monocular SLAM or a 3D sensor. We propose a novel model that uses predicted depths and confidences from CNNs as priors to inpaint maps with arbitrary scale and sparsity. We obtain more reliable reconstructions than those of traditional depth inpainting methods such as the cross-bilateral filter that in comparison offer few learnable parameters. Here we advocate the idea of "just-in-time reconstruction" where a higher level of scene understanding reliably inpaints the corresponding portion of a sparse map on-demand and in real-time.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201