CVPR 2024 Papers

In radio astronomy visibility data which are measurements of wave signals from radio telescopes are transformed into images for observation of distant celestial objects. However these resultant images usually contain both real sources and artifacts due to signal sparsity and other factors. One way to obtain cleaner images is to reconstruct samples into dense forms before imaging. Unfortunately existing reconstruction methods often miss some components of visibility in frequency domain so blurred object edges and persistent artifacts remain in the images. Furthermore the computation overhead is high on irregular visibility samples due to the data skew. To address these problems we propose PolarRec a transformer-encoder-conditioned reconstruction pipeline with visibility samples converted into the polar coordinate system. This coordinate system matches the way in which radio telescopes observe a celestial area as the Earth rotates. As a result visibility samples distribute in the polar system more uniformly than in the Cartesian space. Therefore we propose to use radial distance in the loss function to help reconstruct complete visibility effectively. Also we group visibility samples by their polar angles and propose a group-based encoding scheme to improve the efficiency. Our experiments demonstrate that PolarRec markedly improves imaging results by faithfully reconstructing all frequency components in the visibility domain while significantly reducing the computation cost in visibility data encoding. The code is available at https://github.com/RapidsAtHKUST/PolarRec.

The integration of neural rendering and the SLAM system recently showed promising results in joint localization and photorealistic view reconstruction. However existing methods fully relying on implicit representations are so resource-hungry that they cannot run on portable devices which deviates from the original intention of SLAM. In this paper we present Photo-SLAM a novel SLAM framework with a hyper primitives map. Specifically we simultaneously exploit explicit geometric features for localization and learn implicit photometric features to represent the texture information of the observed environment. In addition to actively densifying hyper primitives based on geometric features we further introduce a Gaussian-Pyramid-based training method to progressively learn multi-level features enhancing photorealistic mapping performance. The extensive experiments with monocular stereo and RGB-D datasets prove that our proposed system Photo-SLAM significantly outperforms current state-of-the-art SLAM systems for online photorealistic mapping e.g. PSNR is 30% higher and rendering speed is hundreds of times faster in the Replica dataset. Moreover the Photo-SLAM can run at real-time speed using an embedded platform such as Jetson AGX Orin showing the potential of robotics applications. Project Page and code: https://huajianup.github.io/research/Photo-SLAM/.

We introduce the Panoptic 3D Reconstruction task a unified and holistic scene understanding task for a monocular video. And we present PanoRecon - a novel framework to address this new task which realizes an online geometry reconstruction alone with dense semantic and instance labeling. Specifically PanoRecon incrementally performs panoptic 3D reconstruction for each video fragment consisting of multiple consecutive key frames from a volumetric feature representation using feed-forward neural networks. We adopt a depth-guided back-projection strategy to sparse and purify the volumetric feature representation. We further introduce a voxel clustering module to get object instances in each local fragment and then design a tracking and fusion algorithm for the integration of instances from different fragments to ensure temporal coherence. Such design enables our PanoRecon to yield a coherent and accurate panoptic 3D reconstruction. Experiments on ScanNetV2 demonstrate a very competitive geometry reconstruction result compared with state-of-the-art reconstruction methods as well as promising 3D panoptic segmentation result with only RGB input while being real-time. Code is available at: https://github.com/Riser6/PanoRecon.

Neural implicit scene representations have recently shown encouraging results in dense visual SLAM. However existing methods produce low-quality scene reconstruction and low-accuracy localization performance when scaling up to large indoor scenes and long sequences. These limitations are mainly due to their single global radiance field with finite capacity which does not adapt to large scenarios. Their end-to-end pose networks are also not robust enough with the growth of cumulative errors in large scenes. To this end we introduce PLGSLAM a neural visual SLAM system capable of high-fidelity surface reconstruction and robust camera tracking in real-time. To handle large-scale indoor scenes PLGSLAM proposes a progressive scene representation method which dynamically allocates new local scene representation trained with frames within a local sliding window. This allows us to scale up to larger indoor scenes and improves robustness (even under pose drifts). In local scene representation PLGSLAM utilizes tri-planes for local high-frequency features with multi-layer perceptron (MLP) networks for the low-frequency feature achieving smoothness and scene completion in unobserved areas. Moreover we propose local-to-global bundle adjustment method with a global keyframe database to address the increased pose drifts on long sequences. Experimental results demonstrate that PLGSLAM achieves state-of-the-art scene reconstruction results and tracking performance across various datasets and scenarios (both in small and large-scale indoor environments).

Recent works have proposed end-to-end autonomous vehicle (AV) architectures comprised of differentiable modules achieving state-of-the-art driving performance. While they provide advantages over the traditional perception-prediction-planning pipeline (e.g. removing information bottlenecks between components and alleviating integration challenges) they do so using a diverse combination of tasks modules and their interconnectivity. As of yet however there has been no systematic analysis of the necessity of these modules or the impact of their connectivity placement and internal representations on overall driving performance. Addressing this gap our work conducts a comprehensive exploration of the design space of end-to-end modular AV stacks. Our findings culminate in the development of PARA-Drive: a fully parallel end-to-end AV architecture. PARA-Drive not only achieves state-of-the-art performance in perception prediction and planning but also significantly enhances runtime speed by nearly 3x without compromising on interpretability or safety.

It is time-consuming to render high-resolution images in applications such as video games and virtual reality and thus super-resolution technologies become increasingly popular for real-time rendering. However it is challenging to preserve sharp texture details keep the temporal stability and avoid the ghosting artifacts in real-time super-resolution rendering. To address this issue we introduce radiance demodulation to separate the rendered image or radiance into a lighting component and a material component considering the fact that the light component is smoother than the rendered image so that the high-resolution material component with detailed textures can be easily obtained. We perform the super-resolution on the lighting component only and re-modulate it with the high-resolution material component to obtain the final super-resolution image with more texture details. A reliable warping module is proposed by explicitly marking the occluded regions to avoid the ghosting artifacts. To further enhance the temporal stability we design a frame-recurrent neural network and a temporal loss to aggregate the previous and current frames which can better capture the spatial-temporal consistency among reconstructed frames. As a result our method is able to produce temporally stable results in real-time rendering with high-quality details even in the challenging 4 x4 super-resolution scenarios. Code is available at: \href https://github.com/Riga2/NSRD https://github.com/Riga2/NSRD .

Recent studies have noted an intriguing phenomenon termed Neural Collapse that is when the neural networks establish the right correlation between feature spaces and the training targets their last-layer features together with the classifier weights will collapse into a stable and symmetric structure. In this paper we extend the investigation of Neural Collapse to the biased datasets with imbalanced attributes. We observe that models will easily fall into the pitfall of shortcut learning and form a biased non-collapsed feature space at the early period of training which is hard to reverse and limits the generalization capability. To tackle the root cause of biased classification we follow the recent inspiration of prime training and propose an avoid-shortcut learning framework without additional training complexity. With well-designed shortcut primes based on Neural Collapse structure the models are encouraged to skip the pursuit of simple shortcuts and naturally capture the intrinsic correlations. Experimental results demonstrate that our method induces a better convergence property during training and achieves state-of-the-art generalization performance on both synthetic and real-world biased datasets.

We introduce a novel approach to single image denoising based on the Blind Spot Denoising principle which we call MAsked and SHuffled Blind Spot Denoising (MASH). We focus on the case of correlated noise which often plagues real images. MASH is the result of a careful analysis to determine the relationships between the level of blindness (masking) of the input and the (unknown) noise correlation. Moreover we introduce a shuffling technique to weaken the local correlation of noise which in turn yields an additional denoising performance improvement. We evaluate MASH via extensive experiments on real-world noisy image datasets. We demonstrate state-of-the-art results compared to existing self-supervised denoising methods.

Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains but due to the absence of style diversity during training enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simulated data is a feasible alternative to retrieving large style-diverse real-world datasets as it is a cumbersome and budget-intensive process. However the large domain-specific inconsistencies between simulated and real-world data pose a significant generalization challenge in semantic segmentation. In this work to alleviate this problem we propose a novel Multi-Resolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. Our experimental results on various urban-scene segmentation datasets clearly indicate that along with the perturbation of style-information perturbation of fine-feature components is paramount to learn domain invariant robust feature maps for semantic segmentation models. MRFP is a simple and computationally efficient transferable module with no additional learnable parameters or objective functions that helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation. Code is available at https://github.com/airl-iisc/MRFP.

In the context of autonomous navigation of terrestrial robots the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications where they are used for model based control and/or for localization and mapping. The more recent Embodied AI literature on the other hand focuses on modular or end-to-end agents trained in simulators like Habitat or AI-Thor where the emphasis is put on photo-realistic rendering and scene diversity but high-fidelity robot motion is assigned a less privileged role. The resulting sim2real gap significantly impacts transfer of the trained models to real robotic platforms. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap both in sensing and in actuation. Our agent directly predicts (discretized) velocity commands which are maintained through closed-loop control in the real robot. The behavior of the real robot (including the underlying low-level controller) is identified and simulated in a modified Habitat simulator. Noise models for odometry and localization further contribute in lowering the sim2real gap. We evaluate on real navigation scenarios explore different localization and point goal calculation methods and report significant gains in performance and robustness compared to prior work.

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.

Event camera has significant advantages in capturingdynamic scene information while being prone to noise interferenceparticularly in challenging conditions like lowthreshold and low illumination. However most existing researchfocuses on gentle situations hindering event cameraapplications in realistic complex scenarios. To tackle thislimitation and advance the field we construct a new pairedreal-world event denoising dataset (LED) including 3K sequenceswith 18K seconds of high-resolution (1200*680)event streams and showing three notable distinctions comparedto others: diverse noise levels and scenes largerscalewith high-resolution and high-quality GT. Specificallyit contains stepped parameters and varying illuminationwith diverse scenarios. Moreover based on theproperty of noise events inconsistency and signal eventsconsistency we propose a novel effective denoising framework(DED) using homogeneous dual events to generate theGT with better separating noise from the raw. Furthermorewe design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholdsto realize accurate denoising. The experimental resultsdemonstrate that the remarkable performance of the proposedapproach on different datasets.The dataset and codeare at https://github.com/Yee-Sing/led.

Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level. Many methods leverage data-driven learning due to the intricacies of scene complexity and significant indoor occlusions. Training these methods often requires a large-scale high-quality dataset with aligned and paired shape annotations with real-world scans. Existing datasets are either synthetic or misaligned restricting the performance of data-driven methods on real data. To this end we introduce LASA a Large-scale Aligned Shape Annotation Dataset comprising 10412 high-quality CAD annotations aligned with 920 real-world scene scans from ArkitScenes created manually by professional artists. On this top we propose a novel Diffusion-based Cross-Modal Shape Reconstruction (DisCo) method. It is empowered by a hybrid feature aggregation design to fuse multi-modal inputs and recover high-fidelity object geometries. Besides we present an Occupancy-Guided 3D Object Detection (OccGOD) method and demonstrate that our shape annotations provide scene occupancy clues that can further improve 3D object detection. Supported by LASA extensive experiments show that our methods achieve state-of-the-art performance in both instance-level scene reconstruction and 3D object detection tasks.

This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos. While the classical approaches to model and render virtual humans generally use a textured mesh recent research has developed neural body representations that achieve impressive visual quality. However these models are difficult to render in real-time and their quality degrades when the character is animated with body poses different than the training observations. We propose an animatable human model based on 3D Gaussian Splatting that has recently emerged as a very efficient alternative to neural radiance fields. The body is represented by a set of gaussian primitives in a canonical space which is deformed with a coarse to fine approach that combines forward skinning and local non-rigid refinement. We describe how to learn our Human Gaussian Splatting (HuGS) model in an end-to-end fashion from multi-view observations and evaluate it against the state-of-the-art approaches for novel pose synthesis of clothed body. Our method achieves 1.5 dB PSNR improvement over the state-of-the-art on THuman4 dataset while being able to render in real-time (?80 fps for 512x512 resolution).

We present the first approach to render highly realistic free-viewpoint videos of a human actor in general apparel from sparse multi-view recording to display in real-time at an unprecedented 4K resolution. At inference our method only requires four camera views of the moving actor and the respective 3D skeletal pose. It handles actors in wide clothing and reproduces even fine-scale dynamic detail e.g. clothing wrinkles face expressions and hand gestures. At training time our learning-based approach expects dense multi-view video and a rigged static surface scan of the actor. Our method comprises three main stages. Stage 1 is a skeleton-driven neural approach for high-quality capture of the detailed dynamic mesh geometry. Stage 2 is a novel solution to create a view-dependent texture using four test-time camera views as input. Finally stage 3 comprises a new image-based refinement network rendering the final 4K image given the output from the previous stages. Our approach establishes a new benchmark for real-time rendering resolution and quality using sparse input camera views unlocking possibilities for immersive telepresence.

Human-centric 3D scene understanding has recently drawn increasing attention driven by its critical impact on robotics. However human-centric real-life scenarios are extremely diverse and complicated and humans have intricate motions and interactions. With limited labeled data supervised methods are difficult to generalize to general scenarios hindering real-life applications. Mimicking human intelligence we propose an unsupervised 3D detection method for human-centric scenarios by transferring the knowledge from synthetic human instances to real scenes. To bridge the gap between the distinct data representations and feature distributions of synthetic models and real point clouds we introduce novel modules for effective instance-to-scene representation transfer and synthetic-to-real feature alignment. Remarkably our method exhibits superior performance compared to current state-of-the-art techniques achieving 87.8% improvement in mAP and closely approaching the performance of fully supervised methods (62.15 mAP vs. 69.02 mAP) on HuCenLife Dataset.

It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO. In this paper we propose HMD-Poser the first unified approach to recover full-body motions using scalable sparse observations from HMD and body-worn IMUs. In particular it can support a variety of input scenarios such as HMD HMD+2IMUs HMD+3IMUs etc. The scalability of inputs may accommodate users' choices for both high tracking accuracy and easy-to-wear. A lightweight temporal-spatial feature learning network is proposed in HMD-Poser to guarantee that the model runs in real-time on HMDs. Furthermore HMD-Poser presents online body shape estimation to improve the position accuracy of body joints. Extensive experimental results on the challenging AMASS dataset show that HMD-Poser achieves new state-of-the-art results in both accuracy and real-time performance. We also build a new free-dancing motion dataset to evaluate HMD-Poser's on-device performance and investigate the performance gap between synthetic data and real-captured sensor data. Finally we demonstrate our HMD-Poser with a real-time Avatar-driving application on a commercial HMD. Our code and free-dancing motion dataset are available \href https://pico-ai-team.github.io/hmd-poser here .

Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging especially in the presence of large camera or object motion. Existing methods typically align low dynamic range sequences using optical flow or attention mechanism for deghosting. However they often struggle to handle large complex motions and are computationally expensive. To address these challenges we propose a robust and efficient flow estimator tailored for real-time HDR video reconstruction named HDRFlow. HDRFlow has three novel designs: an HDR-domain alignment loss (HALoss) an efficient flow network with a multi-size large kernel (MLK) and a new HDR flow training scheme. The HALoss supervises our flow network to learn an HDR-oriented flow for accurate alignment in saturated and dark regions. The MLK can effectively model large motions at a negligible cost. In addition we incorporate synthetic data Sintel into our training dataset utilizing both its provided forward flow and backward flow generated by us to supervise our flow network enhancing our performance in large motion regions. Extensive experiments demonstrate that our HDRFlow outperforms previous methods on standard benchmarks. To the best of our knowledge HDRFlow is the first real-time HDR video reconstruction method for video sequences captured with alternating exposures capable of processing 720p resolution inputs at 25ms.

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.