CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Recently many approaches directly operate on point clouds for different tasks. These approaches become more computation and storage demanding when point cloud size is large. To reduce the required computation and storage one possible solution is to sample the point cloud. In this paper we propose the first Learnable Task-Agnostic Point Cloud Sampling (LTA-PCS) framework. Existing task-agnostic point cloud sampling strategy (e.g. FPS) does not consider semantic information of point clouds causing degraded performance on downstream tasks. While learning-based point cloud sampling methods consider semantic information they are task-specific and require task-oriented ground-truth annotations. So they cannot generalize well on different downstream tasks. Our LTA-PCS achieves task-agnostic point cloud sampling without requiring task-oriented labels in which both the geometric and semantic information of points is considered in sampling. Extensive experiments on multiple downstream tasks demonstrate the effectiveness of our LTA-PCS.
Even though Non-rigid Structure-from-Motion (NRSfM) has been extensively studied and great progress has been made there are still key challenges that hinder their broad real-world applications: 1) the inherent motion/rotation ambiguity requires either explicit camera motion recovery with extra constraint or complex Procrustean Alignment; 2) existing low-rank modeling of the global shape can over-penalize drastic deformations in the 3D shape sequence. This paper proposes to resolve the above issues from a spatial-temporal modeling perspective. First we propose a novel Temporally-smooth Procrustean Alignment module that estimates 3D deforming shapes and adjusts the camera motion by aligning the 3D shape sequence consecutively. Our new alignment module remedies the requirement of complex reference 3D shape during alignment which is more conductive to non-isotropic deformation modeling. Second we propose a spatial-weighted approach to enforce the low-rank constraint adaptively at different locations to accommodate drastic spatially-variant deformation reconstruction better. Our modeling outperform existing low-rank based methods and extensive experiments across different datasets validate the effectiveness of our method.
In this paper we present ShapeMatcher a unified self-supervised learning framework for joint shape canonicalization segmentation retrieval and deformation. Given a partially-observed object in an arbitrary pose we first canonicalize the object by extracting point-wise affine invariant features disentangling inherent structure of the object with its pose and size. These learned features are then leveraged to predict semantically consistent part segmentation and corresponding part centers. Next our lightweight retrieval module aggregates the features within each part as its retrieval token and compare all the tokens with source shapes from a pre-established database to identify the most geometrically similar shape. Finally we deform the retrieved shape in the deformation module to tightly fit the input object by harnessing part center guided neural cage deformation. The key insight of ShapeMaker is the simultaneous training of the four highly-associated processes: canonicalization segmentation retrieval and deformation leveraging cross-task consistency losses for mutual supervision. Extensive experiments on synthetic datasets PartNet ComplementMe and real-world dataset Scan2CAD demonstrate that ShapeMatcher surpasses competitors by a large margin. Code is released at https://github.com/Det1999/ShapeMaker.
Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods however undergo significant performance degradation compared with traditional methods that retrain the sparse networks via the whole dataset especially at high sparsity ratios. In this paper we attempt to reconcile this disparity by transposing three cardinal factors that profoundly alter the performance of conventional sparsity into the context of PTS. Our endeavors particularly comprise (1) A base-decayed sparsity objective that promotes efficient knowledge transferring from dense network to the sparse counterpart. (2) A reducing-regrowing search algorithm designed to ascertain the optimal sparsity distribution while circumventing overfitting to the small calibration set in PTS. (3) The employment of dynamic sparse training predicated on the preceding aspects aimed at comprehensively optimizing the sparsity structure while ensuring training stability. Our proposed framework termed UniPTS is validated to be much superior to existing PTS methods across extensive benchmarks. As an illustration it amplifies the performance of POT a recently proposed recipe from 3.9% to 68.6% when pruning ResNet-50 at 90% sparsity ratio on ImageNet.
Recent text-to-3D methods employing diffusion models have made significant advancements in 3D human generation. However these approaches face challenges due to the limitations of text-to-image diffusion models which lack an understanding of 3D structures. Consequently these methods struggle to achieve high-quality human generation resulting in smooth geometry and cartoon-like appearances. In this paper we propose HumanNorm a novel approach for high-quality and realistic 3D human generation. The main idea is to enhance the model's 2D perception of 3D geometry by learning a normal-adapted diffusion model and a normal-aligned diffusion model. The normal-adapted diffusion model can generate high-fidelity normal maps corresponding to user prompts with view-dependent and body-aware text. The normal-aligned diffusion model learns to generate color images aligned with the normal maps thereby transforming physical geometry details into realistic appearance. Leveraging the proposed normal diffusion model we devise a progressive geometry generation strategy and a multi-step Score Distillation Sampling (SDS) loss to enhance the performance of 3D human generation. Comprehensive experiments substantiate HumanNorm's ability to generate 3D humans with intricate geometry and realistic appearances.
Leveraging the proposed normal diffusion model we devise a progressive geometry generation strategy and a multi-step Score Distillation Sampling (SDS) loss to enhance the performance of 3D human generation. Comprehensive experiments substantiate HumanNorm's ability to generate 3D humans with intricate geometry and realistic appearances. HumanNorm outperforms existing text-to-3D methods in both geometry and texture quality. The project page of HumanNorm is https://humannorm.github.io/.
This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL) encompassing both unsupervised and semi-supervised settings. Common approaches to CVGL rely on ground-satellite image pairs and employ label-driven supervised training. However the cost of collecting precise cross-view image pairs hinders the deployment of CVGL in real-life scenarios. Without the pairs CVGL will be more challenging to handle the significant imaging and spatial gaps between ground and satellite images. To this end we propose an unsupervised framework including a cross-view projection to guide the model for retrieving initial pseudo-labels and a fast re-ranking mechanism to refine the pseudo-labels by leveraging the fact that "the perfectly paired ground-satellite image is located in a unique and identical scene". The framework exhibits competitive performance compared with supervised works on three open-source benchmarks. Our code and models will be released on https://github.com/liguopeng0923/UCVGL.
A recent trend among generalizable novel view synthesis methods is to learn a rendering operator acting over single camera rays. This approach is promising because it removes the need for explicit volumetric rendering but it effectively treats target images as collections of independent pixels. Here we propose to learn a global rendering operator acting over all camera rays jointly. We show that the right representation to enable such rendering is a 5-dimensional plane sweep volume consisting of the projection of the input images on a set of planes facing the target camera. Based on this understanding we introduce our Convolutional Global Latent Renderer (ConvGLR) an efficient convolutional architecture that performs the rendering operation globally in a low-resolution latent space. Experiments on various datasets under sparse and generalizable setups show that our approach consistently outperforms existing methods by significant margins.
Comprehensive modeling of the surrounding 3D world is crucial for the success of autonomous driving. However existing perception tasks like object detection road structure segmentation depth & elevation estimation and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development process but comes at the cost of losing an end-to-end unified solution to the problem. In this work we address this limitation by studying camera-based 3D panoptic segmentation aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this we introduce a novel method called PanoOcc which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to validate the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore our method can be easily extended to dense occupancy prediction and has demonstrated promising performance on the Occ3D benchmark. The code will be made available at https://github.com/Robertwyq/PanoOcc.
Neural approaches have shown a significant progress on camera-based reconstruction. But they require either a fairly dense sampling of the viewing sphere or pre-training on an existing dataset thereby limiting their generalizability. In contrast photometric stereo (PS) approaches have shown great potential for achieving high-quality reconstruction under sparse viewpoints. Yet they are impractical because they typically require tedious laboratory conditions are restricted to dark rooms and often multi-staged making them subject to accumulated errors. To address these shortcomings we propose an end-to-end uncalibrated multi-view PS framework for reconstructing high-resolution shapes acquired from sparse viewpoints in a real-world environment. We relax the dark room assumption and allow a combination of static ambient lighting and dynamic near LED lighting thereby enabling easy data capture outside the lab. Experimental validation confirms that it outperforms existing baseline approaches in the regime of sparse viewpoints by a large margin. This allows to bring high accuracy 3D reconstruction from the dark room to the real world while maintaining a reasonable data capture complexity.
Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary classes given a few support images annotated with keypoints. Existing methods only rely on the features extracted at support keypoints to predict or refine the keypoints on query image but a few support feature vectors are local and inadequate for CAPE. Considering that human can quickly perceive potential keypoints of arbitrary objects we propose a novel framework for CAPE based on such potential keypoints (named as meta-points). Specifically we maintain learnable embeddings to capture inherent information of various keypoints which interact with image feature maps to produce meta-points without any support. The produced meta-points could serve as meaningful potential keypoints for CAPE. Due to the inevitable gap between inherency and annotation we finally utilize the identities and details offered by support keypoints to assign and refine meta-points to desired keypoints in query image. In addition we propose a progressive deformable point decoder and a slacked regression loss for better prediction and supervision. Our novel framework not only reveals the inherency of keypoints but also outperforms existing methods of CAPE. Comprehensive experiments and in-depth studies on large-scale MP-100 dataset demonstrate the effectiveness of our framework.
Human perception and understanding is a major domain of computer vision which like many other vision subdomains recently stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose object-centric image datasets such as ImageNet is limited by an important domain shift. On the other hand collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs and temporal (cross-pose) pairs taken from videos in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.
Representing signals using coordinate networks dominates the area of inverse problems recently and is widely applied in various scientific computing tasks. Still there exists an issue of spectral bias in coordinate networks limiting the capacity to learn high-frequency components. This problem is caused by the pathological distribution of the neural tangent kernel's (NTK's) eigenvalues of coordinate networks. We find that this pathological distribution could be improved using the classical batch normalization (BN) which is a common deep learning technique but rarely used in coordinate networks. BN greatly reduces the maximum and variance of NTK's eigenvalues while slightly modifies the mean value considering the max eigenvalue is much larger than the most this variance change results in a shift of eigenvalues' distribution from a lower one to a higher one therefore the spectral bias could be alleviated (see Fig. 1). This observation is substantiated by the significant improvements of applying BN-based coordinate networks to various tasks including the image compression computed tomography reconstruction shape representation magnetic resonance imaging and novel view synthesis.
Scene recovery the restoration of images degraded by adverse weather conditions presents significant challenges for existing methods. Physical models constrained by their inherent assumptions often fail when these assumptions are not met; Deep learning models are powerful they are limited by the diversity of their training datasets leading to poor generalization and high computational demands. To address these limitations we propose the Luminous Flux Prior (LFP) to recover degraded images under diverse adverse weather without learning. Luminous flux a physical measure that reflects image brightness has a rate of change that demonstrates a significant correlation with transmission. Consequently we leverage this rate of change in luminous flux as prior knowledge to estimate transmission which in turn assists in image recovery. This approach reduces dependency on physical parameters and enhances adaptability to various weather. Experimental validation under diverse conditions such as sandstorms underwater environments and haze attests to the robustness of LFP in restoring clear images. With a time complexity of \mathcal O (N\log N) LFP enables real-time recovery making it a suitable for devices with limited computational resources.
Referring Image Segmentation (RIS) aims to segment objects from an image based on a language description. Recent advancements have introduced transformer-based methods that leverage cross-modal dependencies significantly enhancing performance in referring segmentation tasks. These methods are designed such that each query predicts different masks. However RIS inherently requires a single-mask prediction leading to a phenomenon known as Query Collapse where all queries yield the same mask prediction. This reduces the generalization capability of the RIS model for complex or novel scenarios. To address this issue we propose a Multi-modal Query Feature Fusion technique characterized by two innovative designs: (1) Gaussian enhanced Multi-Modal Fusion a novel visual grounding mechanism that enhances overall representation by extracting rich local visual information and global visual-linguistic relationships and (2) A Dynamic Query Module that produces a diverse set of queries through a scoring network where the network selectively focuses on queries for objects referred to in the language description. Moreover we show that including an auxiliary loss to increase the distance between mask representations of different queries further enhances performance and mitigates query collapse. Extensive experiments conducted on four benchmark datasets validate the effectiveness of our framework.
In this paper we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt. However obtaining desired editing results conformed with the editing prompt is nontrivial since there exist two significant challenges including accurate editing of only foreground regions and multi-view consistency given a single-view reference image. To tackle the first challenge we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing aimed at foreground-only manipulation while preserving the background. For the second challenge we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem among different views in image-driven editing. Extensive experiments show that our CustomNeRF produces precise editing results under various real scenes for both text- and image-driven settings. The code is available at: https://github. com/hrz2000/CustomNeRF.
Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications. However current methods are often hampered by the non-volumetric or implicit way they represent a scene. This work introduces SplaTAM an approach that for the first time leverages explicit volumetric representations i.e. 3D Gaussians to enable high-fidelity reconstruction from a single unposed RGB-D camera surpassing the capabilities of existing methods. SplaTAM employs a simple online tracking and mapping system tailored to the underlying Gaussian representation. It utilizes a silhouette mask to elegantly capture the presence of scene density. This combination enables several benefits over prior representations including fast rendering and dense optimization quickly determining if areas have been previously mapped and structured map expansion by adding more Gaussians. Extensive experiments show that SplaTAM achieves up to 2x superior performance in camera pose estimation map construction and novel-view synthesis over existing methods paving the way for more immersive high-fidelity SLAM applications.
In order to mimic the human few-shot learning (FSL) ability better and to make FSL closer to real-world applications this paper proposes a practical FSL (pFSL) setting. pFSL is based on unsupervised pre-trained models (analogous to human prior knowledge) and recognizes many novel classes simultaneously. Compared to traditional FSL pFSL is simpler in its formulation easier to evaluate more challenging and more practical. To cope with the rarity of training examples this paper proposes IbM2 an instance-based max-margin method not only for the new pFSL setting but also works well in traditional FSL scenarios. Based on the Gaussian Annulus Theorem IbM2 converts random noise applied to the instances into a mechanism to achieve maximum margin in the many-way pFSL (or traditional FSL) recognition task. Experiments with various self-supervised pre-training methods and diverse many- or few-way FSL tasks show that IbM2 almost always leads to improvements compared to its respective baseline methods and in most cases the improvements are significant. With both the new pFSL setting and novel IbM2 method this paper shows that practical few-shot learning is both viable and promising.
Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based Kernel-based and Transformer-based methods. We argue that this is due to two limitations: 1) Instance size overestimation by axis-aligned bounding box(AABB) 2) False negative error accumulation from inaccurate box to the refinement phase. In this work we introduce Spherical Mask a novel coarse-to-fine approach based on spherical representation overcoming those two limitations with several benefits. Specifically our coarse detection estimates each instance with a 3D polygon using a center and radial distance predictions which avoids excessive size estimation of AABB. To cut the error propagation in the existing coarse-to-fine approaches we virtually migrate points based on the polygon allowing all foreground points including false negatives to be refined. During inference the proposal and point migration modules run in parallel and are assembled to form binary masks of instances. We also introduce two margin-based losses for the point migration to enforce corrections for the false positives/negatives and cohesion of foreground points significantly improving the performance.
During inference the proposal and point migration modules run in parallel and are assembled to form binary masks of instances. We also introduce two margin-based losses for the point migration to enforce corrections for the false positives/negatives and cohesion of foreground points significantly improving the performance. Experimental results from three datasets such as ScanNetV2 S3DIS and STPLS3D show that our proposed method outperforms existing works demonstrating the effectiveness of the new instance representation with spherical coordinates. The code is available at: https://github.com/yunshin/SphericalMask
Unsupervised visual grounding methods alleviate the issue of expensive manual annotation of image-query pairs by generating pseudo-queries. However existing methods are prone to confusing the spatial relationships between objects and rely on designing complex prompt modules to generate query texts which severely impedes the ability to generate accurate and comprehensive queries due to ambiguous spatial relationships and manually-defined fixed templates. To tackle these challenges we propose a omni-directional language query generation approach for unsupervised visual grounding named Omni-Q. Specifically we develop a 3D spatial relation module to extend the 2D spatial representation to 3D thereby utilizing 3D location information to accurately determine the spatial position among objects. Besides we introduce a spatial graph module leveraging the power of graph structures to establish accurate and diverse object relationships and thus enhancing the flexibility of query generation. Extensive experiments on five public benchmark datasets demonstrate that our method significantly outperforms existing state-of-the-art unsupervised methods by up to 16.17%. In addition when applied in the supervised setting our method can freely save up to 60% human annotations without a loss of performance.