CVPR 2024 Papers

Neural implicit representation of geometric shapes has witnessed considerable advancements in recent years. However common distance field based implicit representations specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes. In this paper we introduce a novel neural implicit representation based on unsigned orthogonal distance fields (UODFs). In UODFs the minimal unsigned distance from any spatial point to the shape surface is defined solely in one orthogonal direction contrasting with the multi-directional determination made by SDF and UDF. Consequently every point in the 3D UODFs can directly access its closest surface points along three orthogonal directions. This distinctive feature leverages the accurate reconstruction of surface points without interpolation errors. We verify the effectiveness of UODFs through a range of reconstruction examples extending from simple watertight or non-watertight shapes to complex shapes that include hollows internal or assembling structures.

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.

Semantic scene completion (SSC) aims to predict complete 3D voxel occupancy and semantics from a single-view RGB-D image and recent SSC methods commonly adopt multi-modal inputs. However our investigation reveals two limitations: ineffective feature learning from single modalities and overfitting to limited datasets. To address these issues this paper proposes a novel SSC framework - Adversarial Modality Modulation Network (AMMNet) - with a fresh perspective of optimizing gradient updates. The proposed AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities and a customized adversarial training scheme leveraging dynamic gradient competition. Specifically the cross-modal modulation adaptively re-calibrates the features to better excite representation potentials from each single modality. The adversarial training employs a minimax game of evolving gradients with customized guidance to strengthen the generator's perception of visual fidelity from both geometric completeness and semantic correctness. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin providing a promising direction for improving the effectiveness and generalization of SSC methods.

Deep Convolutional Neural Networks (DCNNs) have achieved remarkable performance in synthetic aperture radar (SAR) object detection but this comes at the cost of tremendous computational resources partly due to extracting redundant features within a single convolutional layer. Recent works either delve into model compression methods or focus on the carefully-designed lightweight models both of which result in performance degradation. In this paper we propose an efficient convolution module for SAR object detection called SFS-Conv which increases feature diversity within each convolutional layer through a shunt-perceive-select strategy. Specifically we shunt input feature maps into space and frequency aspects. The former perceives the context of various objects by dynamically adjusting receptive field while the latter captures abundant frequency variations and textural features via fractional Gabor transformer. To adaptively fuse features from space and frequency aspects a parameter-free feature selection module is proposed to ensure that the most representative and distinctive information are preserved. With SFS-Conv we build a lightweight SAR object detection network called SFS-CNet. Experimental results show that SFS-CNet outperforms state-of-the-art (SoTA) models on a series of SAR object detection benchmarks while simultaneously reducing both the model size and computational cost.

In the context of autonomous driving the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown widespread success most methods follow the ideas originally designed for 2D images. In this paper we present UniPAD a novel self-supervised learning paradigm applying 3D volumetric differentiable rendering. UniPAD implicitly encodes 3D space facilitating the reconstruction of continuous 3D shape structures and the intricate appearance characteristics of their 2D projections. The flexibility of our method enables seamless integration into both 2D and 3D frameworks enabling a more holistic comprehension of the scenes. We manifest the feasibility and effectiveness of UniPAD by conducting extensive experiments on various 3D perception tasks. Our method significantly improves lidar- camera- and lidar-camera-based baseline by 9.1 7.7 and 6.9 NDS respectively. Notably our pre-training pipeline achieves 73.2 NDS for 3D object detection and 79.4 mIoU for 3D semantic segmentation on the nuScenes validation set achieving state-of-the-art results in comparison with previous methods.

Human action anticipation aims at predicting what people will do in the future based on past observations. In this paper we introduce Uncertainty-aware Action Decoupling Transformer (UADT) for action anticipation. Unlike existing methods that directly predict action in a verb-noun pair format we decouple the action anticipation task into verb and noun anticipations separately. The objective is to make the two decoupled tasks assist each other and eventually improve the action anticipation task. Specifically we propose a two-stream Transformer-based architecture which is composed of a verb-to-noun model and a noun-to-verb model. The verb-to-noun model leverages the verb information to improve the noun prediction and the other way around. We extend the model in a probabilistic manner and quantify the predictive uncertainty of each decoupled task to select features. In this way the noun prediction leverages the most informative and redundancy-free verb features and verb prediction works similarly. Finally the two streams are combined dynamically based on their uncertainties to make the joint action anticipation. We demonstrate the efficacy of our method by achieving state-of-the-art performance on action anticipation benchmarks including EPIC-KITCHENS EGTEA Gaze+ and 50-Salads.

In the literature points and conics have been major features for camera geometric calibration. Although conics are more informative features than points the loss of the conic property under distortion has critically limited the utility of conic features in camera calibration. Many existing approaches addressed conic-based calibration by ignoring distortion or introducing 3D spherical targets to circumvent this limitation. In this paper we present a novel formulation for conic-based calibration using moments. Our derivation is based on the mathematical finding that the first moment can be estimated without bias even under distortion. This allows us to track moment changes during projection and distortion ensuring the preservation of the first moment of the distorted conic. With an unbiased estimator the circular patterns can be accurately detected at the sub-pixel level and can now be fully exploited for an entire calibration pipeline resulting in significantly improved calibration. The entire code is readily available from https://github.com/ChaehyeonSong/discocal.

3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive manual 3D annotations. We propose UnScene3D the first fully unsupervised 3D learning approach for class-agnostic 3D instance segmentation of indoor scans. UnScene3D first generates pseudo masks by leveraging self-supervised color and geometry features to find potential object regions. We operate on a basis of 3D segment primitives enabling efficient representation and learning on high-resolution 3D data. The coarse proposals are then refined through self-training our model on its predictions. Our approach improves over state-of-the-art unsupervised 3D instance segmentation methods by more than 300% Average Precision score demonstrating effective instance segmentation even in challenging cluttered 3D scenes.

Deep unfolding networks (DUNs) renowned for their interpretability and superior performance have invigorated the realm of compressive sensing (CS). Nonetheless existing DUNs frequently suffer from issues related to insufficient feature extraction and feature attrition during the iterative steps. In this paper we propose Unrolling Fixed-point Continuous Network (UFC-Net) a novel deep CS framework motivated by the traditional fixed-point continuous optimization algorithm. Specifically we introduce Convolution-guided Attention Module (CAM) to serve as a critical constituent within the reconstruction phase encompassing tailored components such as Multi-head Attention Residual Block (MARB) Auxiliary Iterative Reconstruction Block (AIRB) etc. MARB effectively integrates multi-head attention mechanisms with convolution to reinforce feature extraction transcending the confinement of localized attributes and facilitating the apprehension of long-range correlations. Meanwhile AIRB introduces auxiliary variables significantly bolstering the preservation of features within each iterative stage. Extensive experiments demonstrate that our proposed UFC-Net achieves remarkable performance both on image CS and CS-magnetic resonance imaging (CS-MRI) in contrast to state-of-the-art methods.

Tackling image degradation due to atmospheric turbulence particularly in dynamic environments remains a challenge for long-range imaging systems. Existing techniques have been primarily designed for static scenes or scenes with small motion. This paper presents the first segment-then-restore pipeline for restoring the videos of dynamic scenes in turbulent environments. We leverage mean optical flow with an unsupervised motion segmentation method to separate dynamic and static scene components prior to restoration. After camera shake compensation and segmentation we introduce foreground/background enhancement leveraging the statistics of turbulence strength and a transformer model trained on a novel noise-based procedural turbulence generator for fast dataset augmentation. Benchmarked against existing restoration methods our approach restores most of the geometric distortion and enhances the sharpness of videos. We make our code simulator and data publicly available to advance the field of video restoration from turbulence: riponcs.github.io/TurbSegRes

Depth completion is a vital task for autonomous driving as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this challenge we introduce Tri-Perspective View Decomposition (TPVD) a novel framework that can explicitly model 3D geometry. In particular (1) TPVD ingeniously decomposes the original point cloud into three 2D views one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPV affinitive neighbors the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result our TPVD outperforms existing methods on KITTI NYUv2 and SUN RGBD. Furthermore we build a novel depth completion dataset named TOFDC which is acquired by the time-of-flight (TOF) sensor and the color camera on smartphones.

Deepfake detection faces a critical generalization hurdle with performance deteriorating when there is a mismatch between the distributions of training and testing data. A broadly received explanation is the tendency of these detectors to be overfitted to forgery-specific artifacts rather than learning features that are widely applicable across various forgeries. To address this issue we propose a simple yet effective detector called LSDA (\underline L atent \underline S pace \underline D ata \underline A ugmentation) which is based on a heuristic idea: representations with a wider variety of forgeries should be able to learn a more generalizable decision boundary thereby mitigating the overfitting of method-specific features (see Fig. 1). Following this idea we propose to enlarge the forgery space by constructing and simulating variations within and across forgery features in the latent space. This approach encompasses the acquisition of enriched domain-specific features and the facilitation of smoother transitions between different forgery types effectively bridging domain gaps. Our approach culminates in refining a binary classifier that leverages the distilled knowledge from the enhanced features striving for a generalizable deepfake detector. Comprehensive experiments show that our proposed method is surprisingly effective and transcends state-of-the-art detectors across several widely used benchmarks.

Learning to optimize (L2O) is an emerging technique to solve mathematical optimization problems with learning-based methods. Although with great success in many real-world scenarios such as wireless communications computer networks and electronic design existing L2O works lack theoretical demonstration of their performance and robustness in out-of-distribution (OOD) scenarios. We address this gap by providing comprehensive proofs. First we prove a sufficient condition for a robust L2O model with homogeneous convergence rates over all In-Distribution (InD) instances. We assume an L2O model achieves robustness for an InD scenario. Based on our proposed methodology of aligning OOD problems to InD problems we also demonstrate that the L2O model's convergence rate in OOD scenarios will deteriorate by an equation of the L2O model's input features. Moreover we propose an L2O model with a concise gradient-only feature construction and a novel gradient-based history modeling method. Numerical simulation demonstrates that our proposed model outperforms the state-of-the-art baseline in both InD and OOD scenarios and achieves up to 10 xconvergence speedup. The code of our method can be found from https://github.com/NetX-lab/GoMathL2O-Official.

Building a generalist agent that can interact with the world is an ultimate goal for humans thus spurring the research for embodied navigation where an agent is required to navigate according to instructions or respond to queries. Despite the major progress attained previous works primarily focus on task-specific agents and lack generalizability to unseen scenarios. Recently LLMs have presented remarkable capabilities across various fields and provided a promising opportunity for embodied navigation. Drawing on this we propose the first generalist model for embodied navigation NaviLLM. It adapts LLMs to embodied navigation by introducing schema-based instruction. The schema-based instruction flexibly casts various tasks into generation problems thereby unifying a wide range of tasks. This approach allows us to integrate diverse data sources from various datasets into the training equipping NaviLLM with a wide range of capabilities required by embodied navigation. We conduct extensive experiments to evaluate the performance and generalizability of our model. The experimental results demonstrate that our unified model achieves state-of-the-art performance on CVDN SOON and ScanQA. Specifically it surpasses the previous stats-of-the-art method by a significant margin of 29% in goal progress on CVDN. Moreover our model also demonstrates strong generalizability and presents impressive results on unseen tasks e.g. embodied question answering and 3D captioning.

In this paper we propose an accurate post-training quantization framework of diffusion models (APQ-DM) for efficient image generation. Conventional quantization frameworks learn shared quantization functions for tensor discretization regardless of the generation timesteps in diffusion models while the activation distribution differs significantly across various timesteps. Meanwhile the calibration images are acquired in random timesteps which fail to provide sufficient information for generalizable quantization function learning. Both issues cause sizable quantization errors with obvious image generation performance degradation. On the contrary we design distribution-aware quantization functions for activation discretization in different timesteps and search the optimal timesteps for informative calibration image generation so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead. Specifically we partition various timestep quantization functions into different groups according to the importance weights which are optimized by differentiable search algorithms. We also extend structural risk minimization principle for informative calibration image generation to enhance the generalization ability in the deployment of quantized diffusion model. Extensive experimental results show that our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost.

The rapid evolution of automatic facial indexing tech- nologies increases the risk of compromising personal and sensitive information. To address the issue we propose cre- ating cartoon avatars or 'toon avatars' designed to effec- tively obscure identity features. The primary objective is to deceive current AI systems preventing them from accu- rately identifying individuals while making minimal modi- fications to their facial features. Moreover we aim to en- sure that a human observer can still recognize the person depicted in these altered avatar images. To achieve this we introduce 'ToonerGAN' a novel approach that utilizes Generative Adversarial Networks (GANs) to craft person- alized cartoon avatars. The ToonerGAN framework con- sists of a style module and a de-identification module that work together to produce high-resolution realistic cartoon images. For the efficient training of our network we have developed an extensive dataset named 'ToonSet' compris- ing approximately 23000 facial images and their cartoon renditions. Through comprehensive experiments and bench- marking against existing datasets including CelebA-HQ our method demonstrates superior performance in obfus- cating identity while preserving the utility of data. Addi- tionally a user-centric study to explore the effectiveness of ToonerGAN has yielded some compelling observations.

Self-supervised image backbones can be used to address complex 2D tasks (e.g. semantic segmentation object discovery) very efficiently and with little or no downstream supervision. Ideally 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results obtained thanks to distillation methods that keep improving. Yet we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work instead of focusing only on the distillation method we study the effect of three pillars for distillation: the 3D backbone the pretrained 2D backbone and the pretraining 2D+3D dataset. In particular thanks to our scalable distillation method named ScaLR we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features and to improve the robustness of the pretrained backbones to domain gaps and perturbations.

Pose refinement is an interesting and practically relevant research direction. Pose refinement can be used to (1) obtain a more accurate pose estimate from an initial prior (e.g. from retrieval) (2) as pre-processing i.e. to provide a better starting point to a more expensive pose estimator (3) as post-processing of a more accurate localizer. Existing approaches focus on learning features / scene representations for the pose refinement task. This involves training an implicit scene representation or learning features while optimizing a camera pose-based loss. A natural question is whether training specific features / representations is truly necessary or whether similar results can be already achieved with more generic features. In this work we present a simple approach that combines pre-trained features with a particle filter and a renderable representation of the scene. Despite its simplicity it achieves state-of-the-art results demonstrating that one can easily build a pose refiner without the need for specific training. The code will be released upon acceptance.