mtybilly/Instruct2DS huggingface dataset:
SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion
Authors: Mohammad Saeed Rad,  Dwarikanath Mahapatra,  Jean-Philippe Thiran Behzad Bozorgtabar
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a supervised mode of learning or stereo images. The former is limited by the amount of labeled data, as it requires expensive sensors, while the latter is not always readily available as monocular sequences. In this work, we demonstrate the benefit of using geometric information from synthetic images, coupled with scene depth information, to recover the scale in depth and ego-motion estimation from monocular videos. We developed our framework using synthetic image-depth pairs and unlabeled real monocular images. We had three training objectives: first, to use deep feature alignment to reduce the domain gap between synthetic and monocular images to yield more accurate depth estimation when presented with only real monocular images at test time. Second, we learn scene specific representation by exploiting self-supervision coming from multi-view synthetic images without the need for depth labels. Third, our method uses single-view depth and pose networks, which are capable of jointly training and supervising one another mutually, yielding consistent depth and ego-motion estimates. Extensive experiments demonstrate that our depth and ego-motion models surpass the state-of-the-art, unsupervised methods and compare favorably to early supervised deep models for geometric understanding. We validate the effectiveness of our training objectives against standard benchmarks thorough an ablation study.
Diverse Image Synthesis From Semantic Layouts via Conditional IMLE
Authors: Tianhao Zhang,  Jitendra Malik Ke Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Most existing methods for conditional image synthesis are only able to generate a single plausible image for any given input, or at best a fixed number of plausible images. In this paper, we focus on the problem of generating images from semantic segmentation maps and present a simple new method that can generate an arbitrary number of images with diverse appearance for the same semantic layout. Unlike most existing approaches which adopt the GAN framework, our method is based on the recently introduced Implicit Maximum Likelihood Estimation (IMLE) framework. Compared to the leading approach, our method is able to generate more diverse images while producing fewer artifacts despite using the same architecture. The learned latent space also has sensible structure despite the lack of supervision that encourages such behaviour.
Towards Bridging Semantic Gap to Improve Semantic Segmentation
Authors: Yazhao Li,  Jianbing Shen,  Ling Shao Yanwei Pang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Aggregating multi-level features is essential for capturing multi-scale context information for precise scene semantic segmentation. However, the improvement by directly fusing shallow features and deep features becomes limited as the semantic gap between them increases. To solve this problem, we explore two strategies for robust feature fusion. One is enhancing shallow features using a semantic enhancement module (SeEM) to alleviate the semantic gap between shallow features and deep features. The other strategy is feature attention, which involves discovering complementary information (i.e., boundary information) from low-level features to enhance high-level features for precise segmentation. By embedding these two strategies, we construct a parallel feature pyramid towards improving multi-level feature fusion. A Semantic Enhanced Network called SeENet is constructed with the parallel pyramid to implement precise segmentation. Experiments on three benchmark datasets demonstrate the effectiveness of our method for robust multi-level feature aggregation. As a result, our SeENet has achieved better performance than other state-of-the-art methods for semantic segmentation.
Generating Diverse and Descriptive Image Captions Using Visual Paraphrases
Authors: Jiajun Tang,  Xiaojun Wan,  Zongming Guo Lixin Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Recently there has been significant progress in image captioning with the help of deep learning. However, captions generated by current state-of-the-art models are still far from satisfactory, despite high scores in terms of conventional metrics such as BLEU and CIDEr. Human-written captions are diverse, informative and precise, but machine-generated captions seem to be simple, vague and dull. In this paper, aimed at improving diversity and descriptiveness characteristics of generated image captions, we propose a model utilizing visual paraphrases (different sentences describing the same image) in captioning datasets. We explore different strategies to select useful visual paraphrase pairs for training by designing a variety of scoring functions. Our model consists of two decoding stages, where a preliminary caption is generated in the first stage and then paraphrased into a more diverse and descriptive caption in the second stage. Extensive experiments are conducted on the benchmark MS COCO dataset, with automatic evaluation and human evaluation results verifying the effectiveness of our model.
Learning to Collocate Neural Modules for Image Captioning
Authors: Hanwang Zhang,  Jianfei Cai Xu Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We do not speak word by word from scratch; our brain quickly structures a pattern like sth do sth at someplace and then fill in the detailed description. To render existing encoder-decoder image captioners such human-like reasoning, we propose a novel framework: learning to Collocate Neural Modules (CNM), to generate the "inner pattern" connecting visual encoder and language decoder. Unlike the widely-used neural module networks in visual Q&A, where the language (i.e., question) is fully observable, CNM for captioning is more challenging as the language is being generated and thus is partially observable. To this end, we make the following technical contributions for CNM training: 1) compact module design --- one for function words and three for visual content words (e.g., noun, adjective, and verb), 2) soft module fusion and multi-step module execution, robustifying the visual reasoning in partial observation, 3) a linguistic loss for module controller being faithful to part-of-speech collocations (e.g., adjective is before noun). Extensive experiments on the challenging MS-COCO image captioning benchmark validate the effectiveness of our CNM image captioner. In particular, CNM achieves a new state-of-the-art 127.9 CIDEr-D on Karpathy split and a single-model 126.0 c40 on the official server. CNM is also robust to few training samples, e.g., by training only one sentence per image, CNM can halve the performance loss compared to a strong baseline.
Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning
Authors: Harsh Agrawal,  Dhruv Batra,  Alexander Schwing Jyoti Aneja
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from object detectors or part-of-speech tags. In common to all those methods is the fact that the latent variable either only initializes the sentence generation process or is identical across the steps of generation. Both methods offer no fine-grained control. To address this concern, we propose Seq-CVAE which learns a latent space for every word. We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future. We illustrate the efficacy of the proposed approach on the challenging MSCOCO dataset, significantly improving diversity metrics compared to baselines while performing on par w.r.t. sentence quality.
Why Does a Visual Question Have Different Answers?
Authors: Qing Li,  Danna Gurari Nilavra Bhattacharya
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Visual question answering is the task of returning the answer to a question about an image. A challenge is that different people often provide different answers to the same visual question. To our knowledge, this is the first work that aims to understand why. We propose a taxonomy of nine plausible reasons, and create two labelled datasets consisting of  45,000 visual questions indicating which reasons led to answer differences. We then propose a novel problem of predicting directly from a visual question which reasons will cause answer differences as well as a novel algorithm for this purpose. Experiments demonstrate the advantage of our approach over several related baselines on two diverse datasets. We publicly share the datasets and code at https://vizwiz.org.
G3raphGround: Graph-Based Language Grounding
Authors: Lanjun Wang,  Leonid Sigal Mohit Bajaj
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this paper we present an end-to-end framework for grounding of phrases in images. In contrast to previous works, our model, which we call GraphGround, uses graphs to formulate more complex, non-sequential dependencies among proposal image regions and phrases. We capture intra-modal dependencies using a separate graph neural network for each modality (visual and lingual), and then use conditional message-passing in another graph neural network to fuse their outputs and capture cross-modal relationships. This final representation results in grounding decisions. The framework supports many-to-many matching and is able to ground single phrase to multiple image regions and vice versa. We validate our design choices through a series of ablation studies and illustrate state-of-the-art performance on Flickr30k and ReferIt Game benchmark datasets.
Scene Text Visual Question Answering
Authors: Ruben Tito,  Andres Mafla,  Lluis Gomez,  Marcal Rusinol,  Ernest Valveny,  C.V. Jawahar,  Dimosthenis Karatzas Ali Furkan Biten
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research.
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM
Authors: Dan Xu,  Wanli Ouyang,  Xiaogang Wang Lu Sheng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this paper we tackle the joint learning problem of keyframe detection and visual odometry towards monocular visual SLAM systems. As an important task in visual SLAM, keyframe selection helps efficient camera relocalization and effective augmentation of visual odometry. To benefit from it, we first present a deep network design for the keyframe selection, which is able to reliably detect keyframes and localize new frames, then an end-to-end unsupervised deep framework further proposed for simultaneously learning the keyframe selection and the visual odometry tasks. As far as we know, it is the first work to jointly optimize these two complementary tasks in a single deep framework. To make the two tasks facilitate each other in the learning, a collaborative optimization loss based on both geometric and visual metrics is proposed. Extensive experiments on publicly available datasets (i.e. KITTI raw dataset and its odometry split) clearly demonstrate the effectiveness of the proposed approach, and new state-of-the-art results are established on the unsupervised depth and pose estimation from monocular videos.
MVSCRF: Learning Multi-View Stereo With Conditional Random Fields
Authors: Jiansheng Chen,  Weitao Wan,  Yiqing Huang,  Cheng Yu,  Tianpeng Li,  Jiayu Bao Youze Xue
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present a deep-learning architecture for multi-view stereo with conditional random fields (MVSCRF). Given an arbitrary number of input images, we first use a U-shape neural network to extract deep features incorporating both global and local information, and then build a 3D cost volume for the reference camera. Unlike previous learning based methods, we explicitly constraint the smoothness of depth maps by using conditional random fields (CRFs) after the stage of cost volume regularization. The CRFs module is implemented as recurrent neural networks so that the whole pipeline can be trained end-to-end. Our results show that the proposed pipeline outperforms previous state-of-the-arts on large-scale DTU dataset. We also achieve comparable results with state-of-the-art learning based methods on outdoor Tanks and Temples dataset without fine-tuning, which demonstrates our method's generalization ability.
Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses
Authors: Carsten Rother Eric Brachmann
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones.
Efficient Learning on Point Clouds With Basis Point Sets
Authors: Christoph Lassner,  Javier Romero Sergey Prokudin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: With an increased availability of 3D scanning technology, point clouds are moving into the focus of computer vision as a rich representation of everyday scenes. However, they are hard to handle for machine learning algorithms due to the unordered structure. One common approach is to apply voxelization, which dramatically increases the amount of data stored and at the same time loses details through discretization. Recently, deep learning models with hand-tailored architectures were proposed to handle point clouds directly and achieve input permutation invariance. However, these architectures use an increased number of parameters and are computationally inefficient. In this work we propose basis point sets as a highly efficient and fully general way to process point clouds with machine learning algorithms. Basis point sets are a residual representation that can be computed efficiently and can be used with standard neural network architectures. Using the proposed representation as the input to a relatively simple network allows us to match the performance of PointNet on a shape classification task while using three order of magnitudes less floating point operations. In a second experiment, we show how proposed representation can be used for obtaining high resolution meshes from noisy 3D scans. Here, our network achieves performance comparable to the state-of-the-art computationally intense multi-step frameworks, in one network pass that can be done in less than 1ms.
Cross View Fusion for 3D Human Pose Estimation
Authors: Chunyu Wang,  Jingdong Wang,  Naiyan Wang,  Wenjun Zeng Haibo Qiu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present an approach to recover absolute 3D human poses from multi-view images by incorporating multi-view geometric priors in our model. It consists of two separate steps: (1) estimating the 2D poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses. First, we introduce a cross-view fusion scheme into CNN to jointly estimate 2D poses for multiple views. Consequently, the 2D pose estimation for each view already benefits from other views. Second, we present a recursive Pictorial Structure Model to recover the 3D pose from the multi-view 2D poses. It gradually improves the accuracy of 3D pose with affordable computational cost. We test our method on two public datasets H36M and Total Capture. The Mean Per Joint Position Errors on the two datasets are 26mm and 29mm, which outperforms the state-of-the-arts remarkably (26mm vs 52mm, 29mm vs 35mm).
Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images
Authors: Ming C. Lin Junbang Liang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model. Use of multi-view images can significantly reduce the projection ambiguity of the problem, increasing the reconstruction accuracy of the 3D human body under clothing. Our experiments show that this method benefits from the synthetic dataset generated from our pipeline since it has good flexibility of variable control and can provide ground-truth for validation. Our method outperforms existing methods on real-world images, especially on shape estimations.
Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations
Authors: Henrique Morimitsu,  Shan Gao,  Xiangyang Ji Yan Di
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this paper, we propose a novel and specially designed method for piecewise dense monocular depth estimation in dynamic scenes. We utilize spatial relations between neighboring superpixels to solve the inherent relative scale ambiguity (RSA) problem and smooth the depth map. However, directly estimating spatial relations is an ill-posed problem. Our core idea is to predict spatial relations based on the corresponding motion relations. Given two or more consecutive frames, we first compute semi-dense (CPM) or dense (optical flow) point matches between temporally neighboring images. Then we develop our method in four main stages: superpixel relations analysis, motion selection, reconstruction, and refinement. The final refinement process helps to improve the quality of the reconstruction at pixel level. Our method does not require per-object segmentation, template priors or training sets, which ensures flexibility in various applications. Extensive experiments on both synthetic and real datasets demonstrate that our method robustly handles different dynamic situations and presents competitive results to the state-of-the-art methods while running much faster than them.
Is This the Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization
Authors: Ignacio Rocco,  Jiri Sedlar,  Masatoshi Okutomi,  Josef Sivic,  Tomas Pajdla,  Torsten Sattler,  Akihiko Torii Hajime Taira
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Visual localization in large and complex indoor scenes, dominated by weakly textured rooms and repeating geometric patterns, is a challenging problem with high practical relevance for applications such as Augmented Reality and robotics. To handle the ambiguities arising in this scenario, a common strategy is, first, to generate multiple estimates for the camera pose from which a given query image was taken. The pose with the largest geometric consistency with the query image, e.g., in the form of an inlier count, is then selected in a second stage. While a significant amount of research has concentrated on the first stage, there has been considerably less work on the second stage. In this paper, we thus focus on pose verification. We show that combining different modalities, namely appearance, geometry, and semantics, considerably boosts pose verification and consequently pose accuracy. We develop multiple hand-crafted as well as a trainable approach to join into the geometric-semantic verification and show significant improvements over state-of-the-art on a very challenging indoor dataset.
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
Authors: Shenlong Wang,  Wei-Chiu Ma,  Rui Hu,  Raquel Urtasun Shivam Duggal
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch module that allows us to discard most disparities without requiring full cost volume evaluation. We then exploit this representation to learn which range to prune for each pixel. By progressively reducing the search space and effectively propagating such information, we are able to efficiently compute the cost volume for high likelihood hypotheses and achieve savings in both memory and computation.Finally, an image guided refinement module is exploited to further improve the performance. Since all our components are differentiable, the full network can be trained end-to-end. Our experiments show that our method achieves competitive results on KITTI and SceneFlow datasets while running in real-time at 62ms.
Convolutional Sequence Generation for Skeleton-Based Action Synthesis
Authors: Zhizhong Li,  Yuanjun Xiong,  Huahan Yan,  Dahua Lin Sijie Yan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this work, we aim to generate long actions represented as sequences of skeletons. The generated sequences must demonstrate continuous, meaningful human actions, while maintaining coherence among body parts. Instead of generating skeletons sequentially following an autoregressive model, we propose a framework that generates the entire sequence altogether by transforming from a sequence of latent vectors sampled from a Gaussian process (GP). This framework, named Convolutional Sequence Generation Network (CSGN), jointly models structures in temporal and spatial dimensions. It captures the temporal structure at multiple scales through the GP prior and the temporal convolutions; and establishes the spatial connection between the latent vectors and the skeleton graphs via a novel graph refining scheme. It is noteworthy that CSGN allows bidirectional transforms between the latent and the observed spaces, thus enabling semantic manipulation of the action sequences in various forms. We conducted empirical studies on multiple datasets, including a set of high-quality dancing sequences collected by us. The results show that our framework can produce long action sequences that are coherent across time steps and among body parts.
Onion-Peel Networks for Deep Video Completion
Authors: Sungho Lee,  Joon-Young Lee,  Seon Joo Kim Seoung Wug Oh
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose the onion-peel networks for video completion. Given a set of reference images and a target image with holes, our network fills the hole by referring the contents in the reference images. Our onion-peel network progressively fills the hole from the hole boundary enabling it to exploit richer contextual information for the missing regions every step. Given a sufficient number of recurrences, even a large hole can be inpainted successfully. To attend to the missing information visible in the reference images, we propose an asymmetric attention block that computes similarities between the hole boundary pixels in the target and the non-hole pixels in the references in a non-local manner. With our attention block, our network can have an unlimited spatial-temporal window size and fill the holes with globally coherent contents. In addition, our framework is applicable to the image completion guided by the reference images without any modification, which is difficult to do with the previous methods. We validate that our method produces visually pleasing image and video inpainting results in realistic test cases.
Copy-and-Paste Networks for Deep Video Inpainting
Authors: Seoung Wug Oh,  DaeYeun Won,  Seon Joo Kim Sungho Lee
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present a novel deep learning based algorithm for video inpainting. Video inpainting is a process of completing corrupted or missing regions in videos. Video inpainting has additional challenges compared to image inpainting due to the extra temporal information as well as the need for maintaining the temporal coherency. We propose a novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video. The network is trained to copy corresponding contents in reference frames and paste them to fill the holes in the target frame. Our network also includes an alignment network that computes homographies between frames for the alignment, enabling the network to take information from more distant frames for robustness. Our method produces visually pleasing and temporally coherent results while running faster than the state-of-the-art optimization-based method. In addition, we extend our framework for enhancing over/under exposed frames in videos. Using this enhancement technique, we were able to significantly improve the lane detection accuracy on road videos.
Content and Style Disentanglement for Artistic Style Transfer
Authors: Artsiom Sanakoyeu,  Sabine Lang,  Bjorn Ommer Dmytro Kotovenko
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Artists rarely paint in a single style throughout their career. More often they change styles or develop variations of it. In addition, artworks in different styles and even within one style depict real content differently: while Picasso's Blue Period displays a vase in a blueish tone but as a whole, his Cubist works deconstruct the object. To produce artistically convincing stylizations, style transfer models must be able to reflect these changes and variations. Recently many works have aimed to improve the style transfer task, but neglected to address the described observations. We present a novel approach which captures particularities of style and the variations within and separates style and content. This is achieved by introducing two novel losses: a fixpoint triplet style loss to learn subtle variations within one style or between different styles and a disentanglement loss to ensure that the stylization is not conditioned on the real input photo. In addition the paper proposes various evaluation methods to measure the importance of both losses on the validity, quality and variability of final stylizations. We provide qualitative results to demonstrate the performance of our approach.
Compositional Video Prediction
Authors: Maneesh Singh,  Abhinav Gupta,  Shubham Tulsiani Yufei Ye
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present an approach for pixel-level future prediction given an input image of a scene. We observe that a scene is comprised of distinct entities that undergo motion and present an approach that operationalizes this insight. We implicitly predict future states of independent entities while reasoning about their interactions, and compose future video frames using these predicted states. We overcome the inherent multi-modality of the task using a global trajectory-level latent random variable, and show that this allows us to sample diverse and plausible futures. We empirically validate our approach against alternate representations and ways of incorporating multi-modality. We examine two datasets, one comprising of stacked objects that may fall, and the other containing videos of humans performing activities in a gym, and show that our approach allows realistic stochastic video prediction across these diverse settings. See project website (https://judyye.github.io/CVP/) for video predictions.
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence
Authors: Lars Petersson, Laurent Kneip, Hongdong Li Dylan Campbell
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence. The evaluation empirically supports the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.
Robust Pseudo Random Fields for Light-Field Stereo Matching
Authors: Chao-Tsung Huang
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Markov Random Fields are widely used to model light-field stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to light-field statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense light fields are not suitable for sparse ones. In this paper, we develop an empirical Bayesian framework--Robust Pseudo Random Field--to explore intrinsic statistical cues for broad applicability. Based on pseudo-likelihood, it applies soft expectation-maximization (EM) for good model fitting and hard EM for robust depth estimation. We introduce novel pixel difference models to enable such adaptability and robustness simultaneously. We also devise an algorithm to employ this framework on dense, sparse, and even denoised light fields. Experimental results show that it estimates scene-dependent parameters robustly and converges quickly. In terms of depth accuracy and computation speed, it also outperforms state-of-the-art algorithms constantly.
A Lightweight Approach for On-The-Fly Reflectance Estimation
Authors: Jinwei Gu, Stephen Tyree, Pavlo Molchanov, Matthias Niessner, Jan Kautz Kihwan Kim
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Estimating surface reflectance (BRDF) is one key component for complete 3D scene capture, with wide applications in virtual reality, augmented reality, and human computer interaction. Prior work is either limited to controlled environments (e.g., gonioreflectometers, light stages or multi-camera domes), or requires the joint optimization of shape, illumination, and reflectance, which is often computationally too expensive (e.g., hours of running time) for real-time applications. Moreover, most prior work requires HDR images as input which further complicates the capture process. In this paper, we propose a lightweight, practical approach for surface reflectance estimation directly from 8-bit RGB images in real-time, which can be easily plugged into any 3D scanning-and-fusion system with a commodity RGBD sensor. Our method is learning-based, with an inference time of less than 90ms per scene and a model size of less than 340K bytes. We propose two novel network architectures, HemiCNN and Grouplet, to deal with the unstructured input data from multiple viewpoints under unknown illumination. We further design a loss function to resolve the color-constancy and scale ambiguity. In addition, we have created a large synthetic dataset, SynBRDF, which comprises a total of 500K RGBD images rendered with a physically-based ray tracer under a variety of natural illumination, covering 5000 materials and 5000 shapes. SynBRDF is the first large-scale benchmark dataset for reflectance estimation. Experiments on both synthetic data and real data show that the proposed method effectively recovers surface reflectance, and outperforms prior work for reflectance estimation in uncontrolled environments.
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus
Authors: Siyu Zhu, Tian Fang, Long Quan Runze Zhang
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we derive the distributed formulation from the classical optimization algorithm ADMM, Alternating Direction Method of Multipliers, based on the global camera consensus. Then, we analyze the conditions under which the convergence of this distributed optimization would be guaranteed. In particular, we adopt over-relaxation and self-adaption schemes to improve the convergence rate. After that, we propose to split the large scale camera-point visibility graph in order to reduce the communication overheads of the distributed computing. The experiments on both public large scale SfM data-sets and our very large scale aerial photo sets demonstrate that the proposed distributed method clearly outperforms the state-of-the-art method in efficiency and accuracy.
Practical Projective Structure From Motion (P2SfM)
Authors: Alessio Del Bue Ludovic Magerand
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: This paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number of unknowns to estimate and improve computational speed by optimizing standard linear Least Squares systems instead of homogeneous ones. In order to do so, we show that an extension of the linear constraints from the Generalized Projective Reconstruction Theorem can be transferred to the projective parameters, ensuring also a valid projective reconstruction in the process. We use an incremental approach that, starting from a solvable sub-problem, incrementally adds views and points until completion with a robust, outliers free, procedure. Experiments with simulated data shows that our approach is performing well, both in term of the quality of the reconstruction and the capacity to handle missing data and outliers with a reduced computational time. Finally, results on real datasets shows the ability of the method to be used in medium and large scale 3D reconstruction scenarios with high ratios of missing data (up to 98%).
Anticipating Daily Intention Using On-Wrist Motion Triggered Sensing
Authors: Ting-An Chien, Cheng-Sheng Chan, Chan-Wei Hu, Min Sun Tz-Ying Wu
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average.
Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction From a Single Image
Authors: Hamed Kiani Galoogahi, Chaoyang Wang, Simon Lucey Rui Zhu
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image. Hitherto, the problem has been addressed through the application of canonical deep learning methods to regress from the image directly to the 3D shape and pose labels. These approaches, however, are problematic from two perspectives. First, they are minimizing the error between 3D shapes and pose labels - with little thought about the nature of this "label error" when reprojecting the shape back onto the image. Second, they rely on the onerous and ill-posed task of hand labeling natural images with respect to 3D shape and pose. In this paper we define the new task of pose-aware shape reconstruction from a single image, and we advocate that cheaper 2D annotations of objects silhouettes in natural images can be utilized. We design architectures of pose-aware shape reconstruction which reproject the predicted shape back on to the image using the predicted pose. Our evaluation on several object categories demonstrates the superiority of our method for predicting pose-aware 3D shapes from natural images.
End-To-End Learning of Geometry and Context for Deep Stereo Regression
Authors: Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, Adam Bry Alex Kendall
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem's geometry to form a cost volume using deep feature representations. We learn to incorporate contextual information using 3-D convolutions over this volume. Disparity values are regressed from the cost volume using a proposed differentiable soft argmin operation, which allows us to train our method end-to-end to sub-pixel accuracy without any additional post-processing or regularization. We evaluate our method on the Scene Flow and KITTI datasets and on KITTI we set a new state-of-the-art benchmark, while being significantly faster than competing approaches.
Using Sparse Elimination for Solving Minimal Problems in Computer Vision
Authors: Janne Heikkila
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Finding a closed form solution to a system of polynomial equations is a common problem in computer vision as well as in many other areas of engineering and science. Groebner basis techniques are often employed to provide the solution, but implementing an efficient Groebner basis solver to a given problem requires strong expertise in algebraic geometry. One can also convert the equations to a polynomial eigenvalue problem (PEP) and solve it using linear algebra, which is a more accessible approach for those who are not so familiar with algebraic geometry. In previous works PEP has been successfully applied for solving some relative pose problems in computer vision, but its wider exploitation is limited by the problem of finding a compact monomial basis. In this paper, we propose a new algorithm for selecting the basis that is in general more compact than the basis obtained with a state-of-the-art algorithm making PEP a more viable option for solving polynomial equations. Another contribution is that we present two minimal problems for camera self-calibration based on homography, and demonstrate experimentally using synthetic and real data that our algorithm can provide a numerically stable solution to the camera focal length from two homographies of unknown planar scene.
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Authors: Zhen Li, Haibin Huang, Evangelos Kalogerakis, Yizhou Yu Xiaoguang Han
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input local 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.
Temporal Tessellation: A Unified Approach for Video Analysis
Authors: Gil Levi, Tal Hassner, Lior Wolf Dotan Kaufman
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: We present a general approach to video understanding, inspired by semantic transfer techniques that have been successfully used for 2D image analysis. Our method considers a video to be a 1D sequence of clips, each one associated with its own semantics. The nature of these semantics -- natural language captions or other labels -- depends on the task at hand. A test video is processed by forming correspondences between its clips and the clips of reference videos with known semantics, following which, reference semantics can be transferred to the test video. We describe two matching methods, both designed to ensure that (a) reference clips appear similar to test clips and (b), taken together, the semantics of the selected reference clips is consistent and maintains temporal coherence. We use our method for video captioning on the LSMDC'16 benchmark, video summarization on the SumMe and TVSum benchmarks, Temporal Action Detection on the Thumos2015 benchmark, and sound prediction on the Greatest Hits benchmark. Our method not only surpasses the state of the art, in four out of five benchmarks, but importantly, it is the only single method we know of that was successfully applied to such a diverse range of tasks.
Learning Policies for Adaptive Tracking With Deep Feature Cascades
Authors: Simon Lucey, Deva Ramanan Chen Huang
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Visual object tracking is a fundamental and time-critical vision task. Recent years have seen many shallow tracking methods based on real-time pixel-based correlation filters, as well as deep methods that have top performance but need a high-end GPU. In this paper, we learn to improve the speed of deep trackers without losing accuracy. Our fundamental insight is to take an adaptive approach, where easy frames are processed with cheap features (such as pixel values), while challenging frames are processed with invariant but expensive deep features. We formulate the adaptive tracking problem as a decision-making process, and learn an agent to decide whether to locate objects with high confidence on an early layer, or continue processing subsequent layers of a network. This significantly reduces the feed-forward cost for easy frames with distinct or slow-moving objects. We train the agent offline in a reinforcement learning fashion, and further demonstrate that learning all deep layers (so as to provide good features for adaptive tracking) can lead to near real-time average tracking speed of 23 fps on a single CPU while achieving state-of-the-art performance. Perhaps most tellingly, our approach provides a 100X speedup for almost 50% of the time, indicating the power of an adaptive approach.