CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
State-of-the-art man-made shape generative models usually adopt established generative models under a suitable implicit shape representation. A common theme is to perform distribution alignment which does not explicitly model important shape priors. As a result many synthetic shapes are not connected. Other synthetic shapes present problems of physical stability and geometric feasibility. This paper introduces a novel latent diffusion shape-generative model regularized by a quality checker that outputs a score of a latent code. The scoring function employs a learned function that provides a geometric feasibility score and a deterministic procedure to quantify a physical stability score. The key to our approach is a new diffusion procedure that combines the discrete empirical data distribution and a continuous distribution induced by the quality checker. We introduce a principled approach to determine the tradeoff parameters for learning the denoising network at different noise levels. Experimental results show that our approach outperforms state-of-the-art shape generations quantitatively and qualitatively on ShapeNet-v2.
Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However these methods exhibit a pervasive enhancement bias towards the compression domain inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their compressed counterparts thus degrading their perceptual quality. In this paper we propose a simple yet effective method to mitigate this bias and enhance the quality of compressed images. Our method employs a conditional discriminator with the compressed image as a key condition and then incorporates a domain-divergence regularization to actively distance the enhancement domain from the compression domain. Through this dual strategy our method enables the discrimination against the compression domain and brings the enhancement domain closer to the raw domain. Comprehensive quality evaluations confirm the superiority of our method over other state-of-the-art methods without incurring inference overheads.
Humans live in a 3D world and commonly use natural language to interact with a 3D scene. Modeling a 3D language field to support open-ended language queries in 3D has gained increasing attention recently. This paper introduces LangSplat which constructs a 3D language field that enables precise and efficient open-vocabulary querying within 3D spaces. Unlike existing methods that ground CLIP language embeddings in a NeRF model LangSplat advances the field by utilizing a collection of 3D Gaussians each encoding language features distilled from CLIP to represent the language field. By employing a tile-based splatting technique for rendering language features we circumvent the costly rendering process inherent in NeRF. Instead of directly learning CLIP embeddings LangSplat first trains a scene-wise language autoencoder and then learns language features on the scene-specific latent space thereby alleviating substantial memory demands imposed by explicit modeling. Existing methods struggle with imprecise and vague 3D language fields which fail to discern clear boundaries between objects. We delve into this issue and propose to learn hierarchical semantics using SAM thereby eliminating the need for extensively querying the language field across various scales and the regularization of DINO features.
Existing methods struggle with imprecise and vague 3D language fields which fail to discern clear boundaries between objects. We delve into this issue and propose to learn hierarchical semantics using SAM thereby eliminating the need for extensively querying the language field across various scales and the regularization of DINO features. Extensive experimental results show that LangSplat significantly outperforms the previous state-of-the-art method LERF by a large margin. Notably LangSplat is extremely efficient achieving a 199 x speedup compared to LERF at the resolution of 1440 x 1080. We strongly recommend readers to check out our video results at https://langsplat.github.io/.
Many existing motion prediction approaches rely on symbolic perception outputs to generate agent trajectories such as bounding boxes road graph information and traffic lights. This symbolic representation is a high-level abstraction of the real world which may render the motion prediction model vulnerable to perception errors (e.g. failures in detecting open-vocabulary obstacles) while missing salient information from the scene context (e.g. poor road conditions). An alternative paradigm is end-to-end learning from raw sensors. However this approach suffers from the lack of interpretability and requires significantly more training resources. In this work we propose tokenizing the visual world into a compact set of scene elements and then leveraging pre-trained image foundation models and LiDAR neural networks to encode all the scene elements in an open-vocabulary manner. The image foundation model enables our scene tokens to encode the general knowledge of the open world while the LiDAR neural network encodes geometry information. Our proposed representation can efficiently encode the multi-frame multi-modality observations with a few hundred tokens and is compatible with most transformer-based architectures. To evaluate our method we have augmented Waymo Open Motion Dataset with camera embeddings. Experiments over Waymo Open Motion Dataset show that our approach leads to significant performance improvements over the state-of-the-art.
Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy success in prior literature is constrained to narrow distributions of images of landmarks and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation multi-task contrastive pretraining and a novel loss function. Additionally our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model PIGEON is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers winning all six games.
We also develop a bot and deploy PIGEON in a blind experiment against humans ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers winning all six games. Our second model PIGEOTTO differs in that it is trained on a dataset of images from Flickr and Wikipedia achieving state-of-the-art results on a wide range of image geolocalization benchmarks outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate planet-scale image geolocalization systems. Our code is available on GitHub.
How to effectively utilize the spectral and spatial characteristics of Hyperspectral Image (HSI) is always a key problem in spectral snapshot reconstruction. Recently the spectra-wise transformer has shown great potential in capturing inter-spectra similarities of HSI but the classic design of the transformer i.e. multi-head division in the spectral (channel) dimension hinders the modeling of global spectral information and results in mean effect. In addition previous methods adopt the normal spatial priors without taking imaging processes into account and fail to address the unique spatial degradation in snapshot spectral reconstruction. In this paper we analyze the influence of multi-head division and propose a novel Spectral-Spatial Rectification (SSR) method to enhance the utilization of spectral information and improve spatial degradation. Specifically SSR includes two core parts: Window-based Spectra-wise Self-Attention (WSSA) and spAtial Rectification Block (ARB). WSSA is proposed to capture global spectral information and account for local differences whereas ARB aims to mitigate the spatial degradation using a spatial alignment strategy. The experimental results on simulation and real scenes demonstrate the effectiveness of the proposed modules and we also provide models at multiple scales to demonstrate the superiority of our approach.
Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images current text-to-image diffusion models still often struggle to accurately interpret and follow complex input text prompts. In contrast to existing models that aim to generate images only with their best effort we introduce Self-correcting LLM-controlled Diffusion (SLD). SLD is a framework that generates an image from the input prompt assesses its alignment with the prompt and performs self-corrections on the inaccuracies in the generated image. Steered by an LLM controller SLD turns text-to-image generation into an iterative closed-loop process ensuring correctness in the resulting image. SLD is not only training-free but can also be seamlessly integrated with diffusion models behind API access such as DALL-E 3 to further boost the performance of state-of-the-art diffusion models. Experimental results show that our approach can rectify a majority of incorrect generations particularly in generative numeracy attribute binding and spatial relationships. Furthermore by simply adjusting the instructions to the LLM SLD can perform image editing tasks bridging the gap between text-to-image generation and image editing pipelines. Our code is available at: https://self-correcting-llm-diffusion.github.io.
We address the challenge of content diversity and controllability in pedestrian simulation for driving scenarios. Recent pedestrian animation frameworks have a significant limitation wherein they primarily focus on either following trajectory or the content of the reference video consequently overlooking the potential diversity of human motion within such scenarios. This limitation restricts the ability to generate pedestrian behaviors that exhibit a wider range of variations and realistic motions and therefore restricts its usage to provide rich motion content for other components in the driving simulation system e.g. suddenly changed motion to which the autonomous vehicle should respond. In our approach we strive to surpass the limitation by showcasing diverse human motions obtained from various sources such as generated human motions in addition to following the given trajectory. The fundamental contribution of our framework lies in combining the motion tracking task with trajectory following which enables the tracking of specific motion parts (e.g. upper body) while simultaneously following the given trajectory by a single policy. This way we significantly enhance both the diversity of simulated human motion within the given scenario and the controllability of the content including language-based control. Our framework facilitates the generation of a wide range of human motions contributing to greater realism and adaptability in pedestrian simulations for driving scenarios.
Advancements in neural signed distance fields (SDFs) have enabled modeling 3D surface geometry from a set of 2D images of real-world scenes. Baking neural SDFs can extract explicit mesh with appearance baked into texture maps as neural features. The baked meshes still have a large memory footprint and require a powerful GPU for real-time rendering. Neural optimization of such large meshes with differentiable rendering pose significant challenges. We propose a method to produce optimized meshes for large unbounded scenes with low triangle budget and high fidelity of geometry and appearance. We achieve this by combining advancements in baking neural SDFs with classical mesh simplification techniques and proposing a joint appearance-geometry refinement step. The visual quality is comparable to or better than state-of-the-art neural meshing and baking methods with high geometric accuracy despite significant reduction in triangle count making the produced meshes efficient for storage transmission and rendering on mobile hardware. We validate the effectiveness of the proposed method on large unbounded scenes from mip-NeRF 360 Tanks & Temples and Deep Blending datasets achieving at-par rendering quality with 73x reduced triangles and 11x reduction in memory footprint.
Conditional diffusion models are powerful generative models that can leverage various types of conditional information such as class labels segmentation masks or text captions. However in many real-world scenarios conditional information may be noisy or unreliable due to human annotation errors or weak alignment. In this paper we propose the Coherence-Aware Diffusion (CAD) a novel method to integrate confidence in conditional information into diffusion models allowing them to learn from noisy annotations without discarding data. We assume that each data point has an associated confidence score that reflects the quality of the conditional information. We then condition the diffusion model on both the conditional information and the confidence score. In this way the model learns to ignore or discount the conditioning when the confidence is low. We show that our method is theoretically sound and empirically effective on various conditional generation tasks. Moreover we show that leveraging confidence generates realistic and diverse samples that respect conditional information better than models trained on cleaned datasets where samples with low confidence have been discarded.
Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However autonomous vehicles and robots in-the-wild require constant re-calibration due to exposure to various environmental factors including vibration and structural stress when cameras are arranged in a wide-baseline configuration. Conventional rectification methods fail in these challenging scenarios: especially for larger vehicles such as autonomous freight trucks and semi-trucks the resulting incorrect rectification severely affects the quality of downstream tasks that use stereo/multi-view data. To tackle these challenges we propose an online rectification approach that operates at real-time rates while achieving high accuracy. We propose a novel learning-based online calibration approach that utilizes stereo correlation volumes built from a feature representation obtained from cross-image attention. Our model is trained to minimize vertical optical flow as proxy rectification constraint and predicts the relative rotation between the stereo pair. The method is real-time and even outperforms conventional methods used for offline calibration and substantially improves downstream stereo depth post-rectification. We release two public datasets (https://light.princeton.edu/online-stereo-recification/) a synthetic and experimental wide baseline dataset to foster further research.
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
Point cloud registration is still a challenging and open problem. For example when the overlap between two point clouds is extremely low geo-only features may be not sufficient. Therefore it is important to further explore how to utilize color data in this task. Under such circumstances we propose ColorPCR for color point cloud registration with multi-stage geometric-color fusion. We design a Hierarchical Color Enhanced Feature Extraction module to extract multi-level geometric-color features and a GeoColor Superpoint Matching Module to encode transformation-invariant geo-color global context for robust patch correspondences. In this way both geometric and color data can be used thus lead to robust performance even under extremely challenging scenarios such as low overlap between two point clouds. To evaluate the performance of our method we colorize 3DMatch/3DLoMatch datasets as Color3DMatch/Color3DLoMatch and evaluations on these datasets demonstrate the effectiveness of our proposed method. Our method achieves state-of-the-art registration recall of 97.5%/88.9% on them.
The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models which may lead to an unsatisfactory compromise. To tackle with this issue we present a novel strategy from the view of shadow transformation in this paper: directly homogenizing the spatial distribution of shadow degradation. Our key design is the random shuffle operation and its corresponding inverse operation. Specifically random shuffle operation stochastically rearranges the pixels across spatial space and the inverse operation recovers the original order. After randomly shuffling the shadow diffuses in the whole image and the degradation appears in a homogenized way which can be effectively processed by the local self-attention layer. Moreover we further devise a new feed forward network with position modeling to exploit image structural information. Based on these elements we construct the final local window based transformer named HomoFormer for image shadow removal. Our HomoFormer can enjoy the linear complexity of local transformers while bypassing challenges of non-uniformity and diversity of shadow. Extensive experiments are conducted to verify the superiority of our HomoFormer across public datasets.
Counterfactual reasoning a fundamental aspect of human cognition involves contemplating alternatives to established facts or past events significantly enhancing our abilities in planning and decision-making. In light of the advancements in current multi-modal large language models we explore their effectiveness in counterfactual reasoning. To facilitate this investigation we introduce a novel dataset C-VQA specifically designed to examine the counterfactual reasoning capabilities of modern multi-modal large language models. This dataset is constructed by infusing original questions with counterfactual presuppositions spanning various types such as numerical and boolean queries. It encompasses a mix of real and synthetic data representing a wide range of difficulty levels. Our thorough evaluations of contemporary vision-language models using this dataset have revealed substantial performance drops with some models showing up to a 40% decrease highlighting a significant gap between current models and human-like vision reasoning capabilities. We hope our dataset will serve as a vital benchmark for evaluating the counterfactual reasoning capabilities of models. Code and dataset are publicly available at https://bzhao.me/C-VQA/.
Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its significance research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this paper we present three novel elements to advance in-vehicle gaze research. Firstly we introduce IVGaze a pioneering dataset capturing in-vehicle gaze collected from 125 individuals and covering a large range of gaze and head within vehicles. Conventional gaze collection systems are inadequate for in-vehicle use. In this dataset we propose a new vision-based solution for in-vehicle gaze collection introducing a refined gaze target calibration method to tackle annotation challenges. Second our research focuses on in-vehicle gaze estimation leveraging the IVGaze. Images of in-vehicle faces often suffer from low resolution prompting our introduction of a gaze pyramid transformer that harnesses transformer-based multilevel features integration. Expanding upon this we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transformation we rotate virtual cameras to normalize images utilizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR showcases state-of-the-art performance on the IVGaze dataset.
Expanding upon this we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transformation we rotate virtual cameras to normalize images utilizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR showcases state-of-the-art performance on the IVGaze dataset. Thirdly we explore a novel strategy for gaze zone classification by extending the GazeDPTR. A foundational tri-plane and project gaze onto these planes are newly defined. Leveraging both positional features from the projection points and visual attributes from images we achieve superior performance compared to relying solely on visual features thereby substantiating the advantage of gaze estimation. The project is available at https://yihua.zone/work/ivgaze
Adapting driving behavior to new environments customs and laws is a long-standing problem in autonomous driving precluding the widespread deployment of autonomous vehicles (AVs). In this paper we present LLaDA a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: https://boyiliee.github.io/llada.