CVPR 2024 论文列表

We present Neural 3D Strokes a novel technique to generate stylized images of a 3D scene at arbitrary novel views from multi-view 2D images. Different from existing methods which apply stylization to trained neural radiance fields at the voxel level our approach draws inspiration from image-to-painting methods simulating the progressive painting process of human artwork with vector strokes. We develop a palette of stylized 3D strokes from basic primitives and splines and consider the 3D scene stylization task as a multi-view reconstruction process based on these 3D stroke primitives. Instead of directly searching for the parameters of these 3D strokes which would be too costly we introduce a differentiable renderer that allows optimizing stroke parameters using gradient descent and propose a training scheme to alleviate the vanishing gradient issue. The extensive evaluation demonstrates that our approach effectively synthesizes 3D scenes with significant geometric and aesthetic stylization while maintaining a consistent appearance across different views. Our method can be further integrated with style loss and image-text contrastive models to extend its applications including color transfer and text-driven 3D scene drawing. Results and code are available at http://buaavrcg.github.io/Neural3DStrokes.

The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT) often manifested as severe deformations fast motion and occlusions. Most methods that solely depend on coarse-grained object cues such as boxes and the overall appearance of the object are susceptible to degradation due to distorted internal relationships of dynamic objects. To address this problem this work proposes NetTrack an efficient generic and affordable tracking framework to introduce fine-grained learning that is robust to dynamicity. Specifically NetTrack constructs a dynamicity-aware association with a fine-grained Net leveraging point-level visual cues. Correspondingly a fine-grained sampler and matching method have been incorporated. Furthermore NetTrack learns object-text correspondence for fine-grained localization. To evaluate MOT in extremely dynamic open-world scenarios a bird flock tracking (BFT) dataset is constructed which exhibits high dynamicity with diverse species and open-world scenarios. Comprehensive evaluation on BFT validates the effectiveness of fine-grained learning on object dynamicity and thorough transfer experiments on challenging open-world benchmarks i.e. TAO TAO-OW AnimalTrack and GMOT-40 validate the strong generalization ability of NetTrack even without finetuning.

Video scene detection aims to temporally link shots for obtaining semantically compact scenes. It is essential for this task to capture scene-distinguishable affinity among shots by similarity assessment. However most methods relies on ordinary shot-to-shot similarities which may inveigle similar shots into being linked even though they are from different scenes and meanwhile hinder dissimilar shots from being blended into a complete scene. In this paper we propose NeighborNet to inject shot contexts into shot-to-shot similarities through carefully exploring the relations between semantic/temporal neighbors of shots over a local time period. In this way shot-to-shot similarities are remeasured as semantic/temporal neighbor-aware similarities so that NeighborNet can learn context embedding into shot features using graph convolutional network. As a result not only do the learned shot features suppress the affinity among similar shots from different scenes but they also promote the affinity among dissimilar shots in the same scene. Experimental results on public benchmark datasets show that our proposed NeighborNet yields substantial improvements in video scene detection especially outperforms released state-of-the-arts by at least 6% in Average Precision (AP). The code is available at https://github.com/ExMorgan-Alter/NeighborNet.

Model quantization is widely used to compress and accelerate deep neural networks. However recent studies have revealed the feasibility of weaponizing model quantization via implanting quantization-conditioned backdoors (QCBs). These special backdoors stay dormant on released full-precision models but will come into effect after standard quantization. Due to the peculiarity of QCBs existing defenses have minor effects on reducing their threats or are even infeasible. In this paper we conduct the first in-depth analysis of QCBs. We reveal that the activation of existing QCBs primarily stems from the nearest rounding operation and is closely related to the norms of neuron-wise truncation errors (i.e. the difference between the continuous fullprecision weights and its quantized version). Motivated by these insights we propose Error-guided Flipped Rounding with Activation Preservation (EFRAP) an effective and practical defense against QCBs. Specifically EFRAP learns a non-nearest rounding strategy with neuron-wise error norm and layer-wise activation preservation guidance flipping the rounding strategies of neurons crucial for backdoor effects but with minimal impact on clean accuracy. Extensive evaluations on benchmark datasets demonstrate that our EFRAP can defeat state-of-the-art QCB attacks under various settings. Code is available here.

We present NeRSP a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. Reflective surface reconstruction is extremely challenging as specular reflections are view-dependent and thus violate the multiview consistency for multiview stereo. On the other hand sparse image inputs as a practical capture setting commonly cause incomplete or distorted results due to the lack of correspondence matching. This paper jointly handles the challenges from sparse inputs and reflective surfaces by leveraging polarized images. We derive photometric and geometric cues from the polarimetric image formation model and multiview azimuth consistency which jointly optimize the surface geometry modeled via implicit neural representation. Based on the experiments on our synthetic and real datasets we achieve the state-of-the-art surface reconstruction results with only 6 views as input.

We propose NeRFiller an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a captured 3D scene or object are missing due to mesh reconstruction failures or a lack of observations (e.g. contact regions such as the bottom of objects or hard-to-reach areas). We approach this challenging 3D inpainting problem by leveraging a 2D inpainting diffusion model. We identify a surprising behavior of these models where they generate more 3D consistent inpaints when images form a 2x2 grid and show how to generalize this behavior to more than four images. We then present an iterative framework to distill these inpainted regions into a single consistent 3D scene. In contrast to related works we focus on completing scenes rather than deleting foreground objects and our approach does not require tight 2D object masks or text. We compare our approach to relevant baselines adapted to our setting on a variety of scenes where NeRFiller creates the most 3D consistent and plausible scene completions. Our project page is at https://ethanweber.me/nerfiller/.

We present a method for automatically modifying a NeRF representation based on a single observation of a non-rigid transformed version of the original scene. Our method defines the transformation as a 3D flowspecifically as a weighted linear blending of rigid transformations of 3D anchor points that are defined on the surface of the scene. In order to identify anchor points we introduce a novel correspondence algorithm that first matches RGB-based pairs then leverages multi-view information and 3D reprojection to robustly filter false positives in two steps. We also introduce a new dataset for exploring the problem of modifying a NeRF scene through a single observation. Our dataset contains 113 scenes leveraging 47 3D assets.We show that our proposed method outperforms NeRF editing methods as well as diffusion-based methods and we also explore different methods for filtering correspondences.

The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis. As a kind of visual media for 3D scene representation compression with high rate-distortion performance is an eternal target. Motivated by advances in neural compression and neural field representation we propose NeRFCodec an end-to-end NeRF compression framework that integrates non-linear transform quantization and entropy coding for memory-efficient scene representation. Since training a non-linear transform directly on a large scale of NeRF feature planes is impractical we discover that pre-trained neural 2D image codec can be utilized for compressing the features when adding content-specific parameters. Specifically we reuse neural 2D image codec but modify its encoder and decoder heads while keeping the other parts of the pre-trained decoder frozen. This allows us to train the full pipeline via supervision of rendering loss and entropy loss yielding the rate-distortion balance by updating the content-specific parameters. At test time the bitstreams containing latent code feature decoder head and other side information are transmitted for communication. Experimental results demonstrate our method outperforms existing NeRF compression methods enabling high-quality novel view synthesis with a memory budget of 0.5 MB.

Neural Radiance Field (NeRF) has been widely recognized for its excellence in novel view synthesis and 3D scene reconstruction. However their effectiveness is inherently tied to the assumption of static scenes rendering them susceptible to undesirable artifacts when confronted with transient distractors such as moving objects or shadows. In this work we propose a novel paradigm namely "Heuristics-Guided Segmentation" (HuGS) which significantly enhances the separation of static scenes from transient distractors by harmoniously combining the strengths of hand-crafted heuristics and state-of-the-art segmentation models thus significantly transcending the limitations of previous solutions. Furthermore we delve into the meticulous design of heuristics introducing a seamless fusion of Structure-from-Motion (SfM)-based heuristics and color residual heuristics catering to a diverse range of texture profiles. Extensive experiments demonstrate the superiority and robustness of our method in mitigating transient distractors for NeRFs trained in non-static scenes. Project page: https://cnhaox.github.io/NeRF-HuGS/

Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing photorealistic views from multi-view images of static scenes but face challenges in dynamic real-world environments with distractors like moving objects shadows and lighting changes. Existing methods manage controlled environments and low occlusion ratios but fall short in render quality especially under high occlusion scenarios. In this paper we introduce NeRF On-the-go a simple yet effective approach that enables the robust synthesis of novel views in complex in-the-wild scenes from only casually captured image sequences. Delving into uncertainty our method not only efficiently eliminates distractors even when they are predominant in captures but also achieves a notably faster convergence speed. Through comprehensive experiments on various scenes our method demonstrates a significant improvement over state-of-the-art techniques. This advancement opens new avenues for NeRF in diverse and dynamic real-world applications.

Neural Rendering representations have significantly contributed to the field of 3D computer vision. Given their potential considerable efforts have been invested to improve their performance. Nonetheless the essential question of selecting training views is yet to be thoroughly investigated. This key aspect plays a vital role in achieving high-quality results and aligns with the well-known tenet of deep learning: "garbage in garbage out". In this paper we first illustrate the importance of view selection by demonstrating how a simple rotation of the test views within the most pervasive NeRF dataset can lead to consequential shifts in the performance rankings of state-of-the-art techniques. To address this challenge we introduce a unified framework for view selection methods and devise a thorough benchmark to assess its impact. Significant improvements can be achieved without leveraging error or uncertainty estimation but focusing on uniform view coverage of the reconstructed object resulting in a training-free approach. Using this technique we show that high-quality renderings can be achieved faster by using fewer views. We conduct extensive experiments on both synthetic datasets and realistic data to demonstrate the effectiveness of our proposed method compared with random conventional error-based and uncertainty-guided view selection.

A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end we generalize classic image analogies from 2D images to NeRFs. We leverage correspondence transfer along semantic affinity that is driven by semantic features from large pre-trained 2D image models to achieve multi-view consistent appearance transfer. Our method allows exploring the mix-and-match product space of 3D geometry and appearance. We show that our method outperforms traditional stylization-based methods and that a large majority of users prefer our method over several typical baselines. Project page: https://mfischer-ucl.github.io/nerf_analogies

We present NeLF-Pro a novel representation to model and reconstruct light fields in diverse natural scenes that vary in extent and spatial granularity. In contrast to previous fast reconstruction methods that represent the 3D scene globally we model the light field of a scene as a set of local light field feature probes parameterized with position and multi-channel 2D feature maps. Our central idea is to bake the scene's light field into spatially varying learnable representations and to query point features by weighted blending of probes close to the camera - allowing for mipmap representation and rendering. We introduce a novel vector-matrix-matrix (VMM) factorization technique that effectively represents the light field feature probes as products of core factors (i.e. VM) shared among local feature probes and a basis factor (i.e. M) - efficiently encoding internal relationships and patterns within the scene.Experimentally we demonstrate that NeLF-Pro significantly boosts the performance of feature grid-based representations and achieves fast reconstruction with better rendering quality while maintaining compact modeling. Project page: sinoyou.github.io/nelf-pro

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. While most existing work assumes a simple and fixed degradation model (e.g. bicubic downsampling) the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently Kong et al. pioneer the investigation of a more suitable training strategy for Blind SR using Dropout. Although such method indeed brings substantial generalization improvements via mitigating overfitting we argue that Dropout simultaneously introduces undesirable side-effect that compromises model's capacity to faithfully reconstruct fine details. We show both the theoretical and experimental analyses in our paper and furthermore we present another easy yet effective training strategy that enhances the generalization ability of the model by simply modulating its first and second-order features statistics. Experimental results have shown that our method could serve as a model-agnostic regularization and outperforms Dropout on seven benchmark datasets including both synthetic and real-world scenarios.

3D visual grounding is a challenging task that often requires direct and dense supervision notably the semantic label for each object in the scene. In this paper we instead study the naturally supervised setting that learns from only 3D scene and QA pairs where prior works underperform. We propose the Language-Regularized Concept Learner (LARC) which uses constraints from language as regularization to significantly improve the accuracy of neuro-symbolic concept learners in the naturally supervised setting. Our approach is based on two core insights: the first is that language constraints (e.g. a word's relation to another) can serve as effective regularization for structured representations in neuro-symbolic models; the second is that we can query large language models to distill such constraints from language properties. We show that LARC improves performance of prior works in naturally supervised 3D visual grounding and demonstrates a wide range of 3D visual reasoning capabilities--from zero-shot composition to data efficiency and transferability. Our method represents a promising step towards regularizing structured visual reasoning frameworks with language-based priors for learning in settings without dense supervision.

In this paper we investigate a new problem called narrative action evaluation (NAE). NAE aims to generate professional commentary that evaluates the execution of an action. Unlike traditional tasks such as score-based action quality assessment and video captioning involving superficial sentences NAE focuses on creating detailed narratives in natural language. These narratives provide intricate descriptions of actions along with objective evaluations. NAE is a more challenging task because it requires both narrative flexibility and evaluation rigor. One existing possible solution is to use multi-task learning where narrative language and evaluative information are predicted separately. However this approach results in reduced performance for individual tasks because of variations between tasks and differences in modality between language information and evaluation information. To address this we propose a prompt-guided multimodal interaction framework. This framework utilizes a pair of transformers to facilitate the interaction between different modalities of information. It also uses prompts to transform the score regression task into a video-text matching task thus enabling task interactivity. To support further research in this field we re-annotate the MTL-AQA and FineGym datasets with high-quality and comprehensive action narration. Additionally we establish benchmarks for NAE. Extensive experiment results prove that our method outperforms separate learning methods and naive multi-task learning methods. Data and code will be released at https://github.com/shiyi-zh0408/NAE_CVPR2024.

We introduced StyleEntity a zero-shot image manipulation model that utilizes named entities as proxies during its training phase. This strategy enables our model to manipulate images using unseen textual descriptions during inference all within a single training phase. Additionally we proposed an inference technique termed Prompt Ensemble Latent Averaging (PELA). PELA averages the manipulation directions derived from various named entities during inference effectively eliminating the noise directions thus achieving stable manipulation. In our experiments StyleEntity exhibited superior performance in a zero-shot setting compared to other methods. The code model weights and datasets is available at https://github.com/feng-zhida/StyleEntity.

We propose NViST a transformer-based model for efficient and generalizable novel-view synthesis from a single image for real-world scenes. In contrast to many methods that are trained on synthetic data object-centred scenarios or in a category-specific manner NViST is trained on MVImgNet a large-scale dataset of casually-captured real-world videos of hundreds of object categories with diverse backgrounds. NViST transforms image inputs directly into a radiance field conditioned on camera parameters via adaptive layer normalisation. In practice NViST exploits fine-tuned masked autoencoder (MAE) features and translates them to 3D output tokens via cross-attention while addressing occlusions with self-attention. To move away from object-centred datasets and enable full scene synthesis NViST adopts a 6-DOF camera pose model and only requires relative pose dropping the need for canonicalization of the training data which removes a substantial barrier to it being used on casually captured datasets. We show results on unseen objects and categories from MVImgNet and even generalization to casual phone captures. We conduct qualitative and quantitative evaluations on MVImgNet and ShapeNet to show that our model represents a step forward towards enabling true in-the-wild generalizable novel-view synthesis from a single image. Project webpage: https://wbjang.github.io/nvist_webpage.

Neural 3D reconstruction from multi-view images has recently attracted increasing attention from the community. Existing methods normally learn a neural field for the whole scene while it is still under-explored how to reconstruct a target object indicated by users. Considering the Segment Anything Model (SAM) has shown effectiveness in segmenting any 2D images in this paper we propose NTO3D a novel high-quality Neural Target Object 3D (NTO3D) reconstruction method which leverages the benefits of both neural field and SAM. We first propose a novel strategy to lift the multi-view 2D segmentation masks of SAM into a unified 3D occupancy field. The 3D occupancy field is then projected into 2D space and generates the new prompts for SAM. This process is iterative until convergence to separate the target object from the scene. After this we then lift the 2D features of the SAM encoder into a 3D feature field in order to improve the reconstruction quality of the target object. NTO3D lifts the 2D masks and features of SAM into the 3D neural field for high-quality neural target object 3D reconstruction. We conduct detailed experiments on several benchmark datasets to demonstrate the advantages of our method. The code will be available at: https://github.com/ucwxb/NTO3D.