CVPR 2024 论文列表

In this paper we introduce GS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance between efficiency and accuracy. Compared to recent SLAM methods employing neural implicit representations our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Specifically we propose an adaptive expansion strategy that adds new or deletes noisy 3D Gaussians in order to efficiently reconstruct new observed scene geometry and improve the mapping of previously observed areas. This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods. Moreover in the pose tracking process an effective coarse-to-fine technique is designed to select reliable 3D Gaussian representations to optimize camera pose resulting in runtime reduction and robust estimation. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica TUM-RGBD datasets. Project page: \href https://gs-slam.github.io/ https://gs-slam.github.io/ .

We propose GS-IR a novel inverse rendering approach based on 3D Gaussian Splatting (GS) that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results. Unlike previous works that use implicit neural representations and volume rendering (e.g. NeRF) which suffer from low expressive power and high computational complexity we extend GS a top-performance representation for novel view synthesis to estimate scene geometry surface material and environment illumination from multi-view images captured under unknown lighting conditions. There are two main problems when introducing GS to inverse rendering: 1) GS does not support producing plausible normal natively; 2) forward mapping (e.g. rasterization and splatting) cannot trace the occlusion like backward mapping (e.g. ray tracing). To address these challenges our GS-IR proposes an efficient optimization scheme that incorporates a depth-derivation-based regularization for normal estimation and a baking-based occlusion to model indirect lighting. The flexible and expressive GS representation allows us to achieve fast and compact geometry reconstruction photorealistic novel view synthesis and effective physically-based rendering. We demonstrate the superiority of our method over baseline methods through qualitative and quantitative evaluations on various challenging scenes.

We present a new approach termed GPS-Gaussian for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end we train our Gaussian parameter regression module on a large amount of human scan data jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.

Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions addressing the limitations (e.g. efficiency and flexibility) imposed by mesh or NeRF-based representations. However a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animations. Second to stabilize and amortize the learning of millions of Gaussians we propose to use implicit neural fields to predict the Gaussian attributes (e.g. colors). Finally to capture fine avatar geometries and extract detailed meshes we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method GAvatar enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality and achieves extremely fast rendering (100 fps) at 1K resolution.

We introduce Gaussian Articulated Template Model (GART) an explicit efficient and expressive representation for non-rigid articulated subject capturing and rendering from monocular videos. GART utilizes a mixture of moving 3D Gaussians to explicitly approximate a deformable subject's geometry and appearance. It takes advantage of a categorical template model prior (SMPL SMAL etc.) with learnable forward skinning while further generalizing to more complex non-rigid deformations with novel latent bones. GART can be reconstructed via differentiable rendering from monocular videos in seconds or minutes and rendered in novel poses faster than 150fps.

3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis. However it often suffers from over-reconstruction during Gaussian densification where high-variance image regions are covered by a few large Gaussians only leading to blur and artifacts in the rendered images. We design a progressive frequency regularization (FreGS) technique to tackle the over-reconstruction issue within the frequency space. Specifically FreGS performs coarse-to-fine Gaussian densification by exploiting low-to-high frequency components that can be easily extracted with low-pass and high-pass filters in the Fourier space. By minimizing the discrepancy between the frequency spectrum of the rendered image and the corresponding ground truth it achieves high-quality Gaussian densification and alleviates the over-reconstruction of Gaussian splatting effectively. Experiments over multiple widely adopted benchmarks (e.g. Mip-NeRF360 Tanks-and-Temples and Deep Blending) show that FreGS achieves superior novel view synthesis and outperforms the state-of-the-art consistently.

We propose FlashAvatar a novel and lightweight 3D animatable avatar representation that could reconstruct a digital avatar from a short monocular video sequence in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade GPU. To achieve this we maintain a uniform 3D Gaussian field embedded in the surface of a parametric face model and learn extra spatial offset to model non-surface regions and subtle facial details. While full use of geometric priors can capture high-frequency facial details and preserve exaggerated expressions proper initialization can help reduce the number of Gaussians thus enabling super-fast rendering speed. Extensive experimental results demonstrate that FlashAvatar outperforms existing works regarding visual quality and personalized details and is almost an order of magnitude faster in rendering speed. Project page: https://ustc3dv.github.io/FlashAvatar/

3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times some work has emerged that aims to extend the functionality of NeRF beyond view synthesis for semantically aware tasks such as editing and segmentation using 3D feature field distillation from 2D foundation models. However these methods have two major limitations: (a) they are limited by the rendering speed of NeRF pipelines and (b) implicitly represented feature fields suffer from continuity artifacts reducing feature quality. Recently 3D Gaussian Splatting has shown state-of-the-art performance on real-time radiance field rendering. In this work we go one step further: in addition to radiance field rendering we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation. This translation is not straightforward: naively incorporating feature fields in the 3DGS framework encounters significant challenges notably the disparities in spatial resolution and channel consistency between RGB images and feature maps. We propose architectural and training changes to efficiently avert this problem. Our proposed method is general and our experiments showcase novel view semantic segmentation language-guided editing and segment anything through learning feature fields from state-of-the-art 2D foundation models such as SAM and CLIP-LSeg. Across experiments our distillation method is able to provide comparable or better results while being significantly faster to both train and render. Additionally to the best of our knowledge we are the first method to enable point and bounding-box prompting for radiance field manipulation by leveraging the SAM model. Project website at: https://feature-3dgs.github.io/

We present DrivingGaussian an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https://github.com/VDIGPKU/DrivingGaussian.

Regression tasks in computer vision such as age estimation or counting are often formulated into classification by quantizing the target space into classes. Yet real-world data is often imbalanced -- the majority of training samples lie in a head range of target values while a minority of samples span a usually larger tail range. By selecting the class quantization one can adjust imbalanced regression targets into balanced classification outputs though there are trade-offs in balancing classification accuracy and quantization error. To improve regression performance over the entire range of data we propose to construct hierarchical classifiers for solving imbalanced regression tasks. The fine-grained classifiers limit the quantization error while being modulated by the coarse predictions to ensure high accuracy. Standard hierarchical classification approaches when applied to the regression problem fail to ensure that predicted ranges remain consistent across the hierarchy. As such we propose a range-preserving distillation process that effectively learns a single classifier from the set of hierarchical classifiers. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks: age estimation crowd counting and depth estimation. Code is available at https://github.com/xhp-hust-2018-2011/HCA.

Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views yet prevailing methods suffer from high training costs and slow inference speed. This paper introduces DNGaussian a depth-regularized framework based on 3D Gaussian radiance fields offering real-time and high-quality few-shot novel view synthesis at low costs. Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting despite it will encounter a geometry degradation when input views decrease. In the Gaussian radiance fields we find this degradation in scene geometry primarily lined to the positioning of Gaussian primitives and can be mitigated by depth constraint. Consequently we propose a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance. To further refine detailed geometry reshaping we introduce Global-Local Depth Normalization enhancing the focus on small local depth changes. Extensive experiments on LLFF DTU and Blender datasets demonstrate that DNGaussian outperforms state-of-the-art methods achieving comparable or better results with significantly reduced memory cost a 25x reduction in training time and over 3000x faster rendering speed. Code is available at: https://github.com/Fictionarry/DNGaussian

Recently high-fidelity scene reconstruction with an optimized 3D Gaussian splat representation has been introduced for novel view synthesis from sparse image sets. Making such representations suitable for applications like network streaming and rendering on low-power devices requires significantly reduced memory consumption as well as improved rendering efficiency. We propose a compressed 3D Gaussian splat representation that utilizes sensitivity-aware vector clustering with quantization-aware training to compress directional colors and Gaussian parameters. The learned codebooks have low bitrates and achieve a compression rate of up to 31 on real-world scenes with only minimal degradation of visual quality. We demonstrate that the compressed splat representation can be efficiently rendered with hardware rasterization on lightweight GPUs at up to 4 higher framerates than reported via an optimized GPU compute pipeline. Extensive experiments across multiple datasets demonstrate the robustness and rendering speed of the proposed approach.

Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in capturing complex 3D scenes with high fidelity. However one persistent challenge that hinders the widespread adoption of NeRFs is the computational bottleneck due to the volumetric rendering. On the other hand 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering achieving very fast rendering speed and promising image quality. However a significant drawback arises as 3DGS entails a substantial number of 3D Gaussians to maintain the high fidelity of the rendered images which requires a large amount of memory and storage. To address this critical issue we place a specific emphasis on two key objectives: reducing the number of Gaussian points without sacrificing performance and compressing the Gaussian attributes such as view-dependent color and covariance. To this end we propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition we propose a compact but effective representation of view-dependent color by employing a grid-based neural field rather than relying on spherical harmonics. Finally we learn codebooks to compactly represent the geometric attributes of Gaussian by vector quantization. With model compression techniques such as quantization and entropy coding we consistently show over 25x reduced storage and enhanced rendering speed while maintaining the quality of the scene representation compared to 3DGS. Our work provides a comprehensive framework for 3D scene representation achieving high performance fast training compactness and real-time rendering. Our project page is available at https://maincold2.github.io/c3dgs/.

Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand methods requiring extensively calibrated multi-view setups are prohibitively complex and resource-intensive limiting their practical applicability. On the other hand while single-camera Neural Radiance Fields (NeRFs) offer a more streamlined approach they have excessive training and rendering costs. 3D Gaussian Splatting would be a suitable alternative but for two reasons. Firstly existing methods for 3D dynamic Gaussians require synchronized multi-view cameras and secondly the lack of controllability in dynamic scenarios. We present CoGS a method for Controllable Gaussian Splatting that enables the direct manipulation of scene elements offering real-time control of dynamic scenes without the prerequisite of pre-computing control signals. We evaluated CoGS using both synthetic and real-world datasets that include dynamic objects that differ in degree of difficulty. In our evaluations CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.

While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis it relies heavily on accurately pre-computed camera poses. To relax this constraint multiple efforts have been made to train Neural Radiance Fields (NeRFs) without pre-processed camera poses. However the implicit representations of NeRFs provide extra challenges to optimize the 3D structure and camera poses at the same time. On the other hand the recently proposed 3D Gaussian Splatting provides new opportunities given its explicit point cloud representations. This paper leverages both the explicit geometric representation and the continuity of the input video stream to perform novel view synthesis without any SfM preprocessing. We process the input frames in a sequential manner and progressively grow the 3D Gaussians set by taking one input frame at a time without the need to pre-compute the camera poses. Our method significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes. Our project page is: https: //oasisyang.github.io/colmap-free-3dgs.

Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end we introduce Animatable Gaussians a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar we learn a parametric template from the input videos and then parameterize the template on two front & back canonical Gaussian maps where each pixel represents a 3D Gaussian. The learned template is adaptive to the wearing garments for modeling looser clothes like dresses. Such template-guided 2D parameterization enables us to employ a powerful StyleGAN-based CNN to learn the pose-dependent Gaussian maps for modeling detailed dynamic appearances. Furthermore we introduce a pose projection strategy for better generalization given novel poses. Overall our method can create lifelike avatars with dynamic realistic and generalized appearances. Experiments show that our method outperforms other state-of-the-art approaches. Code: https://github.com/lizhe00/AnimatableGaussians.

Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars real-time performance has mostly been demonstrated for static scenes only. To address this we propose ASH an animatable Gaussian splatting approach for photorealistic rendering of dynamic humans in real time. We parameterize the clothed human as animatable 3D Gaussians which can be efficiently splatted into image space to generate the final rendering. However naively learning the Gaussian parameters in 3D space poses a severe challenge in terms of compute. Instead we attach the Gaussians onto a deformable character model and learn their parameters in 2D texture space which allows leveraging efficient 2D convolutional architectures that easily scale with the required number of Gaussians. We benchmark ASH with competing methods on pose-controllable avatars demonstrating that our method outperforms existing real-time methods by a large margin and shows comparable or even better results than offline methods.

We introduce an approach that creates animatable human avatars from monocular videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields (NeRFs) achieve high-quality novel-view/novel-pose image synthesis but often require days of training and are extremely slow at inference time. Recently the community has explored fast grid structures for efficient training of clothed avatars. Albeit being extremely fast at training these methods can barely achieve an interactive rendering frame rate with around 15 FPS. In this paper we use 3D Gaussian Splatting and learn a non-rigid deformation network to reconstruct animatable clothed human avatars that can be trained within 30 minutes and rendered at real-time frame rates (50+ FPS). Given the explicit nature of our representation we further introduce as-isometric-as-possible regularizations on both the Gaussian mean vectors and the covariance matrices enhancing the generalization of our model on highly articulated unseen poses. Experimental results show that our method achieves comparable and even better performance compared to state-of-the-art approaches on animatable avatar creation from a monocular input while being 400x and 250x faster in training and inference respectively.