CVPR 2024 Papers

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.

Traffic scene perception in computer vision is a critically important task to achieve intelligent cities. To date most existing datasets focus on autonomous driving scenes. We observe that the models trained on those driving datasets often yield unsatisfactory results on traffic monitoring scenes. However little effort has been put into improving the traffic monitoring scene understanding mainly due to the lack of specific datasets. To fill this gap we introduce a specialized traffic monitoring dataset termed TSP6K containing images from the traffic monitoring scenario with high-quality pixel-level and instance-level annotations. The TSP6K dataset captures more crowded traffic scenes with several times more traffic participants than the existing driving scenes. We perform a detailed analysis of the dataset and comprehensively evaluate previous popular scene parsing methods instance segmentation methods and unsupervised domain adaption methods. Furthermore considering the vast difference in instance sizes we propose a detail refining decoder for scene parsing which recovers the details of different semantic regions in traffic scenes owing to the proposed TSP6K dataset. Experiments show its effectiveness in parsing the traffic monitoring scenes. Code and dataset are available at https://github.com/PengtaoJiang/TSP6K.

While pre-trained large-scale vision models have shown significant promise for semantic correspondence their features often struggle to grasp the geometry and orientation of instances. This paper identifies the importance of being geometry-aware for semantic correspondence and reveals a limitation of the features of current foundation models under simple post-processing. We show that incorporating this information can markedly enhance semantic correspondence performance with simple but effective solutions in both zero-shot and supervised settings. We also construct a new challenging benchmark for semantic correspondence built from an existing animal pose estimation dataset for both pre-training validating models. Our method achieves a PCK@0.10 score of 65.4 (zero-shot) and 85.6 (supervised) on the challenging SPair-71k dataset outperforming the state of the art by 5.5p and 11.0p absolute gains respectively. Our code and datasets are publicly available at: https://telling-left-from-right.github.io.

3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric scenes. However prevailing methodologies primarily focus on voxel-wise feature aggregation while neglecting instance semantics and scene context. In this paper we present a novel paradigm termed Symphonies (Scene-from-Insts) that delves into the integration of instance queries to orchestrate 2D-to-3D reconstruction and 3D scene modeling. Leveraging our proposed Serial Instance-Propagated Attentions Symphonies dynamically encodes instance-centric semantics facilitating intricate interactions between the image and volumetric domains. Simultaneously Symphonies fosters holistic scene comprehension by capturing context through the efficient fusion of instance queries alleviating geometric ambiguities such as occlusion and perspective errors through contextual scene reasoning. Experimental results demonstrate that Symphonies achieves state-of-the-art performance on the challenging SemanticKITTI and SSCBench-KITTI-360 benchmarks yielding remarkable mIoU scores of 15.04 and 18.58 respectively. These results showcase the promising advancements of our paradigm. The code for our method is available at https://github.com/hustvl/Symphonies.

We propose SketchINR to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts the xy point coordinates in a sketch at each time and stroke. Despite its simplicity SketchINR outperforms existing representations at multiple tasks: (i) Encoding an entire sketch dataset into a fixed size latent vector SketchINR gives 60x and 10x data compression over raster and vector sketches respectively. (ii) SketchINR's auto-decoder provides a much higher-fidelity representation than other learned vector sketch representations and is uniquely able to scale to complex vector sketches such as FS-COCO. (iii) SketchINR supports parallelisation that can decode/render 100x faster than other learned vector representations such as SketchRNN. (iv) SketchINR for the first time emulates the human ability to reproduce a sketch with varying abstraction in terms of number and complexity of strokes. As a first look at implicit sketches SketchINR's compact high-fidelity representation will support future work in modelling long and complex sketches.

Learning from a limited amount of data namely Few-Shot Learning stands out as a challenging computer vision task. Several works exploit semantics and design complicated semantic fusion mechanisms to compensate for rare representative features within restricted data. However relying on naive semantics such as class names introduces biases due to their brevity while acquiring extensive semantics from external knowledge takes a huge time and effort. This limitation severely constrains the potential of semantics in Few-Shot Learning. In this paper we design an automatic way called Semantic Evolution to generate high-quality semantics. The incorporation of high-quality semantics alleviates the need for complex network structures and learning algorithms used in previous works. Hence we employ a simple two-layer network termed Semantic Alignment Network to transform semantics and visual features into robust class prototypes with rich discriminative features for few-shot classification. The experimental results show our framework outperforms all previous methods on six benchmarks demonstrating a simple network with high-quality semantics can beat intricate multi-modal modules on few-shot classification tasks. Code is available at https://github.com/zhangdoudou123/SemFew.

A novel algorithm called semantic line combination detector (SLCD) to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First we generate various line combinations from reliable lines. Second we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. Moreover it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection symmetry axis detection and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD.

We propose SNI-SLAM a semantic SLAM system utilizing neural implicit representation that simultaneously performs accurate semantic mapping high-quality surface reconstruction and robust camera tracking. In this system we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition to fully utilize the correlation between multiple attributes of the environment we integrate appearance geometry and semantic features through cross-attention for feature collaboration. This strategy enables a more multifaceted understanding of the environment thereby allowing SNI-SLAM to remain robust even when single attribute is defective. Then we design an internal fusion-based decoder to obtain semantic RGB Truncated Signed Distance Field (TSDF) values from multi-level features for accurate decoding. Furthermore we propose a feature loss to update the scene representation at the feature level. Compared with low-level losses such as RGB loss and depth loss our feature loss is capable of guiding the network optimization on a higher-level. Our SNI-SLAM method demonstrates superior performance over all recent NeRF-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets while also showing excellent capabilities in accurate semantic segmentation and real-time semantic mapping. Codes will be available at https://github.com/IRMVLab/SNI-SLAM.

We investigate whether region-based representations are effective for recognition. Regions were once a mainstay in recognition approaches but pixel and patch-based features are now used almost exclusively. We show that recent class-agnostic segmenters like SAM can be effectively combined with strong unsupervised representations like DINOv2 and used for a wide variety of tasks including semantic segmentation object-based image retrieval and multi-image analysis. Once the masks and features are extracted these representations even with linear decoders enable competitive performance making them well suited to applications that require custom queries. The compactness of the representation also makes it well-suited to video analysis and other problems requiring inference across many images.

Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and background disturbances involved establishing robust correspondences between audio and visual contents poses unique challenges due to (1) complex entanglement across sound sources and (2) frequent changes in the occurrence of distinct sound events. Assuming sound events occur independently the multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We are motivated to decompose the multi-source audio semantics into single-source semantics for more effective interactions with visual content. We propose a semantic decomposition method based on product quantization where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics. Furthermore we introduce a global-to-local quantization mechanism which distills knowledge from stable global (clip-level) features into local (frame-level) ones to handle frequent changes in audio semantics. Extensive experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance eg +21.2% mIoU on the challenging AVS-Semantic benchmark with ResNet50 backbone.

Semantic scene completion also known as semantic occupancy prediction can provide dense geometric and semantic information for autonomous vehicles which attracts the increasing attention of both academia and industry. Unfortunately existing methods usually formulate this task as a voxel-wise classification problem and treat each voxel equally in 3D space during training. As the hard voxels have not been paid enough attention the performance in some challenging regions is limited. The 3D dense space typically contains a large number of empty voxels which are easy to learn but require amounts of computation due to handling all the voxels uniformly for the existing models. Furthermore the voxels in the boundary region are more challenging to differentiate than those in the interior. In this paper we propose HASSC approach to train the semantic scene completion model with hardness-aware design. The global hardness from the network optimization process is defined for dynamical hard voxel selection. Then the local hardness with geometric anisotropy is adopted for voxel-wise refinement. Besides self-distillation strategy is introduced to make training process stable and consistent. Extensive experiments show that our HASSC scheme can effectively promote the accuracy of the baseline model without incurring the extra inference cost. Source code is available at: https://github.com/songw-zju/HASSC.

The consistency training (CT)-based semi-supervised learning (SSL) bites state-of-the-art performance on SSL-based image classification. However the existing CT-based SSL methods do not highlight the non-Euclidean characteristics and class-wise varieties of embedding spaces in an SSL model thus they cannot fully utilize the effectiveness of CT. Thus we propose a metric tensor-based consistency regularization exploiting the class-variant geometrical structure of embeddings on the high-dimensional feature space. The proposed method not only minimizes the prediction discrepancy between different views of a given image but also estimates the intrinsic geometric curvature of embedding spaces by employing the global and local metric tensors. The global metric tensor is used to globally estimate the class-invariant embeddings from the whole data distribution while the local metric tensor is exploited to estimate the class-variant embeddings of each cluster. The two metric tensors are optimized by the consistency regularization based on the weak and strong augmentation strategy. The proposed method provides the highest classification accuracy on average compared to the existing state-of-the-art SSL methods on conventional datasets.

LiDAR localization is a fundamental task in robotics and computer vision which estimates the pose of a LiDAR point cloud within a global map. Scene Coordinate Regression (SCR) has demonstrated state-of-the-art performance in this task. In SCR a scene is represented as a neural network which outputs the world coordinates for each point in the input point cloud. However SCR treats all points equally during localization ignoring the fact that not all objects are beneficial for localization. For example dynamic objects and repeating structures often negatively impact SCR. To address this problem we introduce LiSA the first method that incorporates semantic awareness into SCR to boost the localization robustness and accuracy. To avoid extra computation or network parameters during inference we distill the knowledge from a segmentation model to the original SCR network. Experiments show the superior performance of LiSA on standard LiDAR localization benchmarks compared to state-of-the-art methods. Applying knowledge distillation not only preserves high efficiency but also achieves higher localization accuracy than introducing extra semantic segmentation modules. We also analyze the benefit of semantic information for LiDAR localization. Our code is released at https://github.com/Ybchun/LiSA.

Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics. However existing self-supervised methods for this problem assume perfect input shape alignment restricting their real-world applicability. In this work we introduce a novel self-supervised Rotation-Invariant 3D correspondence learner with Local Shape Transform dubbed RIST that learns to establish dense correspondences between shapes even under challenging intra-class variations and arbitrary orientations. Specifically RIST learns to dynamically formulate an SO(3)-invariant local shape transform for each point which maps the SO(3)-equivariant global shape descriptor of the input shape to a local shape descriptor. These local shape descriptors are provided as inputs to our decoder to facilitate point cloud self- and cross-reconstruction. Our proposed self-supervised training pipeline encourages semantically corresponding points from different shapes to be mapped to similar local shape descriptors enabling RIST to establish dense point-wise correspondences. RIST demonstrates state-of-the-art performances on 3D part label transfer and semantic keypoint transfer given arbitrarily rotated point cloud pairs outperforming existing methods by significant margins.

Recent self-supervised models produce visual features that are not only effective at encoding image-level but also pixel-level semantics. They have been reported to obtain impressive results for dense visual semantic correspondence estimation even outperforming fully-supervised methods. Nevertheless these models still fail in the presence of challenging image characteristics such as symmetries and repeated parts. To address these limitations we propose a new semantic correspondence estimation method that supplements state-of-the-art self-supervised features with 3D understanding via a weak geometric spherical prior. Compared to more involved 3D pipelines our model provides a simple and effective way of injecting informative geometric priors into the learned representation while requiring only weak viewpoint information. We also propose a new evaluation metric that better accounts for repeated part and symmetry-induced mistakes. We show that our method succeeds in distinguishing between symmetric views and repeated parts across many object categories in the challenging SPair-71k dataset and also in generalizing to previously unseen classes in the AwA dataset.

Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset which contains diverse training environments (e.g. scaffold size etc.) flat contexts cannot sufficiently address its high heterogeneity. Thus a new challenge is posed to generate more semantically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper we propose a novel approach to generate hierarchical semantic environments for each graph. Firstly given an input graph we explicitly extract variant subgraphs from the input graph to generate proxy predictions on local environments. Then stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environments in a hierarchical manner. In addition we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Particularly in the challenging dataset DrugOOD our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks respectively.

Recent methods for label-free 3D semantic segmentation aim to assist 3D model training by leveraging the open-world recognition ability of pre-trained vision language models. However these methods usually suffer from inconsistent and noisy pseudo-labels provided by the vision language models. To address this issue we present a hierarchical intra-modal correlation learning framework that captures visual and geometric correlations in 3D scenes at three levels: intra-set intra-scene and inter-scene to help learn more compact 3D representations. We refine pseudo-labels using intra-set correlations within each geometric consistency set and align features of visually and geometrically similar points using intra-scene and inter-scene correlation learning. We also introduce a feedback mechanism to distill the correlation learning capability into the 3D model. Experiments on both indoor and outdoor datasets show the superiority of our method. We achieve a state-of-the-art 36.6% mIoU on the ScanNet dataset and a 23.0% mIoU on the nuScenes dataset with improvements of 7.8% mIoU and 2.2% mIoU compared with previous SOTA. We also provide theoretical analysis and qualitative visualization results to discuss the mechanism and conduct thorough ablation studies to support the effectiveness of our framework.

Dilated convolution which expands the receptive field by inserting gaps between its consecutive elements is widely employed in computer vision. In this study we propose three strategies to improve individual phases of dilated convolution from the view of spectrum analysis. Departing from the conventional practice of fixing a global dilation rate as a hyperparameter we introduce Frequency-Adaptive Dilated Convolution (FADC) which dynamically adjusts dilation rates spatially based on local frequency components. Subsequently we design two plug-in modules to directly enhance effective bandwidth and receptive field size. The Adaptive Kernel (AdaKern) module decomposes convolution weights into low-frequency and high-frequency components dynamically adjusting the ratio between these components on a per-channel basis. By increasing the high-frequency part of convolution weights AdaKern captures more high-frequency components thereby improving effective bandwidth. The Frequency Selection (FreqSelect) module optimally balances high- and low-frequency components in feature representations through spatially variant reweighting. It suppresses high frequencies in the background to encourage FADC to learn a larger dilation thereby increasing the receptive field for an expanded scope. Extensive experiments on segmentation and object detection consistently validate the efficacy of our approach. The code is made publicly available at https://github.com/Linwei-Chen/FADC.