GenAI4ELab/papercli-papers huggingface dataset:
Few-Shot Single-View 3D Reconstruction with Memory Prior Contrastive Network
Authors: Zhen Xing; Yijiang Chen; Zhixin Ling; Xiangdong Zhou; Yu Xiang
Venue: ECCV 2022 (main)

Abstract: "3D reconstruction of novel categories based on few-shot learning is appealing in real-world applications and attracts increasing research interests. Previous approaches mainly focus on how to design shape prior models for different categories. Their performance on unseen categories is not very competitive. In this paper, we present a Memory Prior Contrastive Network (MPCN) that can store shape prior knowledge in a few-shot learning based 3D reconstruction framework. With the shape memory, a multi-head attention module is proposed to capture different parts of a candidate shape prior and fuse these parts together to guide 3D reconstruction of novel categories. Besides, we introduce a 3D-aware contrastive learning method, which can not only complement the retrieval accuracy of memory network, but also better organize image features for downstream tasks. Compared with previous few-shot 3D reconstruction methods, MPCN can handle the inter-class variability without category annotations. Experimental results on a benchmark synthetic dataset and the Pascal3D+ real-world dataset show that our model outperforms the current state-of-the-art methods significantly."
DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
Authors: Liang Peng; Xiaopei Wu; Zheng Yang; Haifeng Liu; Deng Cai
Venue: ECCV 2022 (main)

Abstract: "Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. The most challenging sub-task lies in the instance depth estimation. Previous works usually use a direct estimation method. However, in this paper we point out that the instance depth on the RGB image is non-intuitive. It is coupled by visual depth clues and instance attribute clues, making it hard to be directly learned in the network. Therefore, we propose to reformulate the instance depth to the combination of the instance visual surface depth (visual depth) and the instance attribute depth (attribute depth). The visual depth is related to objects’ appearances and positions on the image. By contrast, the attribute depth relies on objects’ inherent attributes, which are invariant to the object affine transformation on the image. Correspondingly, we decouple the 3D location uncertainty into visual depth uncertainty and attribute depth uncertainty. By combining different types of depths and associated uncertainties, we can obtain the final instance depth. Furthermore, data augmentation in monocular 3D detection is usually limited due to the physical nature, hindering the boost of performance. Based on the proposed instance depth disentanglement strategy, we can alleviate this problem. Evaluated on KITTI, our method achieves new state-of-the-art results, and extensive ablation studies validate the effectiveness of each component in our method. The codes are released at https://github.com/SPengLiang/DID-M3D."
Adaptive Co-Teaching for Unsupervised Monocular Depth Estimation
Authors: Weisong Ren; Lijun Wang; Yongri Piao; Miao Zhang; Huchuan Lu; Ting Liu
Venue: ECCV 2022 (main)

Abstract: "Unsupervised depth estimation using photometric losses suffers from local minimum and training instability. We address this issue by proposing an adaptive co-teaching framework to distill the learned knowledge from unsupervised teacher networks to a student network. We design an ensemble architecture for our teacher networks, integrating a depth basis decoder with multiple depth coefficient decoders. Depth prediction can then be formulated as a combination of the predicted depth bases weighted by coefficients. By further constraining their correlations, multiple coefficient decoders can yield a diversity of depth predictions, serving as the ensemble teachers. During the co-teaching step, our method allows different supervision sources from not only ensemble teachers but also photometric losses to constantly compete with each other, and adaptively select the optimal ones to teach the student, which effectively improves the ability of the student to jump out of the local minimum. Our method is shown to significantly benefit unsupervised depth estimation and sets new state of the art on both KITTI and Nuscenes datasets."
Fusing Local Similarities for Retrieval-Based 3D Orientation Estimation of Unseen Objects
Authors: Chen Zhao; Yinlin Hu; Mathieu Salzmann
Venue: ECCV 2022 (main)

Abstract: "In this paper, we tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images. This task contrasts with the one considered by most existing deep learning methods which typically assume that the testing objects have been observed during training. To handle the unseen objects, we follow a retrieval-based strategy and prevent the network from learning object-specific features by computing multi-scale local similarities between the query image and synthetically-generated reference images. We then introduce an adaptive fusion module that robustly aggregates the local similarities into a global similarity score of pairwise images. Furthermore, we speed up the retrieval process by developing a fast retrieval strategy. Our experiments on the LineMOD, LineMOD-Occluded, and T-LESS datasets show that our method yields a significantly better generalization to unseen objects than previous works. Our code and pre-trained models are available at https://sailor-z.github.io/projects/Unseen_Object_Pose.html."
Lidar Point Cloud Guided Monocular 3D Object Detection
Authors: Liang Peng; Fei Liu; Zhengxu Yu; Senbo Yan; Dan Deng; Zheng Yang; Haifeng Liu; Deng Cai
Venue: ECCV 2022 (main)

Abstract: "Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird’s-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG."
Structural Causal 3D Reconstruction
Authors: Weiyang Liu; Zhen Liu; Liam Paull; Adrian Weller; Bernhard Schölkopf
Venue: ECCV 2022 (main)

Abstract: "This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images. Due to ambiguity and intrinsic ill-posedness, this problem is inherently difficult to solve and therefore requires strong regularization to achieve disentanglement of different latent factors. Unlike existing works that introduce explicit regularizations into objective functions, we look into a different space for implicit regularization -- the structure of latent space. Specifically, we restrict the structure of latent space to capture a topological causal ordering of latent factors (i.e., representing causal dependency as a directed acyclic graph). We first show that different causal orderings matter for 3D reconstruction, and then explore several approaches to find a task-dependent causal factor ordering. Our experiments demonstrate that the latent space structure indeed serves as an implicit regularization and introduces an inductive bias beneficial for reconstruction."
3D Human Pose Estimation Using Möbius Graph Convolutional Networks
Authors: Niloofar Azizi; Horst Possegger; Emanuele Rodolà; Horst Bischof
Venue: ECCV 2022 (main)

Abstract: "3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks(GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures. However, a major limitation of GCNs is their inability to encode all the transformations between joints explicitly. To address this issue, we propose a novel spectral GCN using the MÃ¶bius transformation (MÃ¶biusGCN). In particular, this allows us to directly and explicitly encode the transformation between joints, resulting in a significantly more compact representation. Compared to even the lightest architectures so far, our novel approach requires 90-98% fewer parameters, i.e. our lightest MÃ¶biusGCN uses only 0.042M trainable parameters. Besides the drastic parameter reduction, explicitly encoding the transformation of joints also enables us to achieve state-of-the-art results. We evaluate our approach on the two challenging pose estimation benchmarks, Human3.6M and MPI-INF-3DHP, demonstrating both state-of-the-art results and the generalization capabilities of MÃ¶biusGCN."
Learning to Train a Point Cloud Reconstruction Network without Matching
Authors: Tianxin Huang; Xuemeng Yang; Jiangning Zhang; Jinhao Cui; Hao Zou; Jun Chen; Xiangrui Zhao; Yong Liu
Venue: ECCV 2022 (main)

Abstract: "Reconstruction networks for well-ordered data such as 2D images and 1D continuous signals are easy to optimize through element-wised squared errors, while permutation-arbitrary point clouds cannot be constrained directly because their points permutations are not fixed. Though existing works design algorithms to match two point clouds and evaluate shape errors based on matched results, they are limited by pre-defined matching processes. In this work, we propose a novel framework named PCLossNet which learns to train a point cloud reconstruction network without any matching. By training through an adversarial process together with the reconstruction network, PCLossNet can better explore the differences between point clouds and create more precise reconstruction results. Experiments on multiple datasets prove the superiority of our method, where PCLossNet can help networks achieve much lower reconstruction errors and extract more representative features, with about 4 times faster training efficiency than the commonly-used EMD loss. Our codes can be found in https://github.com/Tianxinhuang/PCLossNet."
PanoFormer: Panorama Transformer for Indoor 360° Depth Estimation
Authors: Zhijie Shen; Chunyu Lin; Kang Liao; Lang Nie; Zishuo Zheng; Yao Zhao
Venue: ECCV 2022 (main)

Abstract: "Existing panoramic depth estimation methods based on convolutional neural networks (CNNs) focus on removing panoramic distortions, failing to perceive panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the panorama Transformer (named PanoFormer) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and panorama specific metrics. In particular, we divide patches on the spherical tangent domain into tokens to reduce the negative effect of panoramic distortions. Since the geometric structures are essential for depth estimation, a self-attention module is redesigned with an additional learnable token flow. In addition, considering the characteristic of the spherical domain, we present two panorama-specific metrics to comprehensively evaluate the panoramic depth estimation models’ performance. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art methods. At last, the proposed method can be effectively extended to solve semantic panorama segmentation, a similar pixel2pixel task. Code will be released upon acceptance."
Self-supervised Human Mesh Recovery with Cross-Representation Alignment
Authors: Xuan Gong; Meng Zheng; Benjamin Planche; Srikrishna Karanam; Terrence Chen; David Doermann; Ziyan Wu
Venue: ECCV 2022 (main)

Abstract: "Fully supervised human mesh recovery methods are data-hungry and have poor generalizability due to the limited availability and diversity of 3D-annotated benchmark datasets. Recent progress in self-supervised human mesh recovery has been made using synthetic-data-driven training paradigms where the model is trained from synthetic paired 2D representation (e.g., 2D keypoints and segmentation masks) and 3D mesh. However, on synthetic dense correspondence maps (i.e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation. To alleviate this domain gap on IUV, we propose cross-representation alignment utilizing the complementary information from the robust but sparse representation (2D keypoints). Specifically, the alignment errors between initial mesh estimation and both 2D representations are forwarded into regressor and dynamically corrected in the following mesh regression. This adaptive cross-representation alignment explicitly learns from the deviations and captures complementary information: robustness from sparse representation and richness from dense representation. We conduct extensive experiments on multiple standard benchmark datasets and demonstrate competitive results, helping take a step towards reducing the annotation effort needed to produce state-of-the-art models in human mesh estimation."
AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction
Authors: Zerui Chen; Yana Hasson; Cordelia Schmid; Ivan Laptev
Venue: ECCV 2022 (main)

Abstract: "Recent work achieved impressive progress towards joint reconstruction of hands and manipulated objects from monocular color images. Existing methods focus on two alternative representations in terms of either parametric meshes or signed distance fields (SDFs). On one side, parametric models can benefit from prior knowledge at the cost of limited shape deformations and mesh resolutions. Mesh models, hence, may fail to precisely reconstruct details such as contact surfaces of hands and objects. SDF-based methods, on the other side, can represent arbitrary details but are lacking explicit priors. In this work we aim to improve SDF models using priors provided by parametric representations. In particular, we propose a joint learning framework that disentangles the pose and the shape. We obtain hand and object poses from parametric models and use them to align SDFs in 3D space. We show that such aligned SDFs better focus on reconstructing shape details and improve reconstruction accuracy both for hands and objects. We evaluate our method and demonstrate significant improvements over the state of the art on the challenging ObMan and DexYCB benchmarks."
A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation
Authors: Yiming Qian; James H. Elder
Venue: ECCV 2022 (main)

Abstract: "Linear perspective cues deriving from regularities of the built environment can be used to recalibrate both intrinsic and extrinsic camera parameters online, but these estimates can be unreliable due to irregularities in the scene, uncertainties in line segment estimation and background clutter. Here we address this challenge through four initiatives. First, we use the PanoContext panoramic image dataset to curate a novel and realistic dataset of planar projections over a broad range of scenes, focal lengths and camera poses. Second, we use this novel dataset and the YorkUrbanDB to systematically evaluate the linear perspective deviation measures frequently found in the literature and show that the choice of deviation measure and likelihood model has a huge impact on reliability. Third, we use these findings to create a novel system for online camera calibration we call fR, and show that it outperforms the prior state of the art, substantially reducing error in estimated camera rotation and focal length. Our fourth contribution is a novel and efficient approach to estimating uncertainty that can dramatically improve online reliability for performance-critical applications by strategically selecting which frames to use for recalibration."
PS-NeRF: Neural Inverse Rendering for Multi-View Photometric Stereo
Authors: Wenqi Yang; Guanying Chen; Chaofeng Chen; Zhenfang Chen; Kwan-Yee K. Wong
Venue: ECCV 2022 (main)

Abstract: "Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural inverse rendering method for MVPS based on implicit representation. Given multi-view images of a non-Lambertian object illuminated by multiple unknown directional lights, our method jointly estimates the geometry, materials, and lights. Our method first employs multi-light images to estimate per-view surface normal maps, which are used to regularize the normals derived from the neural radiance field. It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer. After optimization, the reconstructed object can be used for novel-view rendering, relighting, and material editing. Experiments on both synthetic and real datasets demonstrate that our method achieves far more accurate shape reconstruction than existing MVPS and neural rendering methods. Our code and model can be found at https://ywq.github.io/psnerf."
Share with Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
Authors: Tom Monnier; Matthew Fisher; Alexei A. Efros; Mathieu Aubry
Venue: ECCV 2022 (main)

Abstract: "Approaches for single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or symmetry. We avoid all such supervision and assumptions by explicitly leveraging the consistency between images of different object instances. As a result, our method can learn from large collections of unlabelled images depicting the same object category. Our main contributions are two ways for leveraging cross-instance consistency: (i) progressive conditioning, a training strategy to gradually specialize the model from category to instances in a curriculum learning fashion; and (ii) neighbor reconstruction, a loss enforcing consistency between instances having similar shape or texture. Also critical to the success of our method are: our structured autoencoding architecture decomposing an image into explicit shape, texture, pose, and background; an adapted formulation of differential rendering; and a new optimization scheme alternating between 3D and pose learning. We compare our approach, UNICORN, both on the diverse synthetic ShapeNet dataset - the classical benchmark for methods requiring multiple views as supervision - and on standard real-image benchmarks (Pascal3D+ Car, CUB) for which most methods require known templates and silhouette annotations. We also showcase applicability to more challenging real-world collections (CompCars, LSUN), where silhouettes are not available and images are not cropped around the object."
Towards Comprehensive Representation Enhancement in Semantics-Guided Self-Supervised Monocular Depth Estimation
Authors: Jingyuan Ma; Xiangyu Lei; Nan Liu; Xian Zhao; Shiliang Pu
Venue: ECCV 2022 (main)

Abstract: "Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics. However, since depth estimation and semantic segmentation are fundamentally two types of tasks: one is regression while the other is classification, the distribution of depth feature and semantic feature are naturally different. Previous works that leverage semantic information in depth estimation mostly neglect such representational discrimination, which leads to insufficient representation enhancement of depth feature. In this work, we propose an attention-based module to enhance task-specific feature by addressing their feature uniqueness within instances. Additionally, we propose a metric learning based approach to accomplish comprehensive enhancement on depth feature by creating a separation between instances in feature space. Extensive experiments and analysis demonstrate the effectiveness of our proposed method. In the end, our method achieves the state-of-the-art performance on KITTI dataset."
AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture
Authors: Zhe Li; Zerong Zheng; Hongwen Zhang; Chaonan Ji; Yebin Liu
Venue: ECCV 2022 (main)

Abstract: "To address the ill-posed problem caused by partial observations in monocular human volumetric capture, we present AvatarCap, a novel framework that introduces animatable avatars into the capture pipeline for high-fidelity reconstruction in both visible and invisible regions. Our method firstly creates an animatable avatar for the subject from a small number ( 20) of 3D scans as a prior. Then given a monocular RGB video of this subject, our method integrates information from both the image observation and the avatar prior, and accordingly reconstructs high-fidelity 3D textured models with dynamic details regardless of the visibility. To learn an effective avatar for volumetric capture from only few samples, we propose GeoTexAvatar, which leverages both geometry and texture supervisions to constrain the pose-dependent dynamics in a decomposed implicit manner. An avatar-conditioned volumetric capture method that involves a canonical normal fusion and a reconstruction network is further proposed to integrate both image observations and avatar dynamics for high-fidelity reconstruction in both observed and invisible regions. Overall, our method enables monocular human volumetric capture with detailed and pose-dependent dynamics, and the experiments show that our method outperforms state of the art."
Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers
Authors: Junhyeong Cho; Kim Youwang; Tae-Hyun Oh
Venue: ECCV 2022 (main)

Abstract: "Transformer encoder architectures have recently achieved state-of-the-art results on monocular 3D human mesh reconstruction, but they require a substantial number of parameters and expensive computations. Due to the large memory overhead and slow inference speed, it is difficult to deploy such models for practical use. In this paper, we propose a novel transformer encoder-decoder architecture for 3D human mesh reconstruction from a single image, called FastMETRO. We identify the performance bottleneck in the encoder-based transformers is caused by the token design which introduces high complexity interactions among input tokens. We disentangle the interactions via an encoder-decoder architecture, which allows our model to demand much fewer parameters and shorter inference time. In addition, we impose the prior knowledge of human body’s morphological relationship via attention masking and mesh upsampling operations, which leads to faster convergence with higher accuracy. Our FastMETRO improves the Pareto-front of accuracy and efficiency, and clearly outperforms image-based methods on Human3.6M and 3DPW. Furthermore, we validate its generalizability on FreiHAND."