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检索条件"主题词=3D from multi-view and sensors"

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Adaptive Annealing for Robust Geometric Estimation

Adaptive Annealing for Robust Geometric Estimation

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sidhartha, Chitturi Manam, Lalit Govindu, Venu Madhav Indian Inst Sci Bengaluru 560012 India

ISBN: (纸本)9798350301298

Geometric estimation problems in vision are often solved via minimization of statistical loss functions which account for the presence of outliers in the observations. The corresponding energy landscape often has many local minima. Many approaches attempt to avoid local minima by annealing the scale parameter of loss functions using methods such as graduated non-convexity (GNC). However, little attention has been paid to the annealing schedule, which is often carried out in a fixed manner, resulting in a poor speed-accuracy trade-off and unreliable convergence to the global minimum. In this paper, we propose a principled approach for adaptively annealing the scale for GNC by tracking the positive-definiteness (i.e. local convexity) of the Hessian of the cost function. We illustrate our approach using the classic problem of registering 3d correspondences in the presence of noise and outliers. We also develop approximations to the Hessian that significantly speeds up our method. The effectiveness of our approach is validated by comparing its performance with state-of-the-art 3d registration approaches on a number of synthetic and real datasets. Our approach is accurate and efficient and converges to the global solution more reliably than the state-of-the-art methods.

关键词： 3d from multi-view and sensors

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FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization

FreeNeRF: Improving Few-shot Neural Rendering with Free Freq...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang, Jiawei Pavone, Marco Wang, Yue UC Los Angeles Los Angeles CA 90095 USA Stanford Univ Nvidia Res Stanford CA USA Nvidia Res Santa Clara CA USA

ISBN: (纸本)9798350301298

Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields (NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as pre-trained models and extra depth signals, or by using non-trivial patch-based rendering. In this paper, we present Frequency regularized NeRF (FreeNeRF), a surprisingly simple baseline that outperforms previous methods with minimal modifications to plain NeRF. We analyze the key challenges in few-shot neural rendering and find that frequency plays an important role in NeRF's training. Based on this analysis, we propose two regularization terms: one to regularize the frequency range of NeRF's inputs, and the other to penalize the near-camera density fields. Both techniques are "free lunches" that come at no additional computational cost. We demonstrate that even with just one line of code change, the original NeRF can achieve similar performance to other complicated methods in the few-shot setting. FreeNeRF achieves state-of-the-art performance across diverse datasets, including Blender, dTU, and LLFF. We hope that this simple baseline will motivate a rethinking of the fundamental role of frequency in NeRF's training, under both the low-data regime and beyond. This project is released at FreeNeRF.

关键词： 3d from multi-view and sensors

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F²-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories

F<SUP>2</SUP>-NeRF: Fast Neural Radiance Field Training with...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Peng Liu, Yuan Chen, Zhaoxi Liu, Lingjie Liu, Ziwei Kornura, Taku Theobalt, Christian Wang, Wenping Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ SLab Singapore Singapore Max Planck Inst Informat Saarbrucken Germany Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9798350301298

This paper presents a novel grid-based NeRF called F-2-NeRF (Fast-Free-NeRF) for novel view synthesis, which enables arbitrary input camera trajectories and only costs a few minutes for training. Existing fast grid-based NeRF training frameworks, like Instant-NGP, Plenoxels, dVGO, or TensoRF, are mainly designed for bounded scenes and rely on space warping to handle unbounded scenes. Existing two widely-used space-warping methods are only designed for the forward-facing trajectory or the 360 degrees object-centric trajectory but cannot process arbitrary trajectories. In this paper, we delve deep into the mechanism of space warping to handle unbounded scenes. Based on our analysis, we further propose a novel space-warping method called perspective warping, which allows us to handle arbitrary trajectories in the grid-based NeRF framework. Extensive experiments demonstrate that F2-NeRF is able to use the same perspective warping to render high-quality images on two standard datasets and a new free trajectory dataset collected by us. Project page: ***/projects/f2-nerf.

关键词： 3d from multi-view and sensors

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Panoptic Compositional Feature Field for Editable Scene Rendering with Network-Inferred Labels via Metric Learning

Panoptic Compositional Feature Field for Editable Scene Rend...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cheng, Xinhua Wu, Yanmin Jia, Mengxi Wang, Qian Zhang, Jian Peking Univ Shenzhen Grad Sch Beijing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China

ISBN: (纸本)9798350301298

despite neural implicit representations demonstrating impressive high-quality view synthesis capacity, decomposing such representations into objects for instance-level editing is still challenging. Recent works learn object-compositional representations supervised by ground truth instance annotations and produce promising scene editing results. However, ground truth annotations are manually labeled and expensive in practice, which limits their usage in real-world scenes. In this work, we attempt to learn an object-compositional neural implicit representation for editable scene rendering by leveraging labels inferred from the off-the-shelf 2d panoptic segmentation networks instead of the ground truth annotations. We propose a novel framework named Panoptic Compositional Feature Field (PCFF), which introduces an instance quadruplet metric learning to build a discriminating panoptic feature space for reliable scene editing. In addition, we propose semantic-related strategies to further exploit the correlations between semantic and appearance attributes for achieving better rendering results. Experiments on multiple scene datasets including ScanNet, Replica, and Toydesk demonstrate that our proposed method achieves superior performance for novel view synthesis and produces convincing real-world scene editing results.

关键词： 3d from multi-view and sensors

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Learning Neural duplex Radiance Fields for Real-Time view Synthesis

Learning Neural Duplex Radiance Fields for Real-Time View Sy...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wan, Ziyu Richardt, Christian Bozic, Aljaz Li, Chao Rengarajan, Vijay Nam, Sconghycon Xiang, Xiaoyu Li, Tuotuo Zhu, Bo Ranjan, Rakesh Liao, Jing City Univ Hong Kong Hong Kong Peoples R China Meta Real Labs Irvine CA USA

ISBN: (纸本)9798350301298

Neural radiance fields (NeRFs) enable novel-view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations - for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations that are fully compatible with the massively parallel graphics rendering pipeline. We represent scenes as neural radiance features encoded on a two-layer duplex mesh, which effectively overcomes the inherent inaccuracies in 3d surface reconstruction by learning the aggregated radiance information from a reliable interval of ray-surface intersections. To exploit local geometric relationships of nearby pixels, we leverage screen-space convolutions instead of the MLPs used in NeRFs to achieve high-quality appearance. Finally, the performance of the whole framework is further boosted by a novel multi-view distillation optimization strategy. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets.

关键词： 3d from multi-view and sensors

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GINA-3d: Learning to Generate Implicit Neural Assets in the Wild

GINA-3D: Learning to Generate Implicit Neural Assets in the ...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shen, Bokui Yan, Xinchen Qi, Charles R. Najibi, Mahyar deng, Boyang Guibas, Leonidas Zhou, Yin Anguelov, dragomir Stanford Univ Stanford CA 94305 USA Waymo LLC Mountain View CA USA Google Mountain View CA USA

ISBN: (纸本)9798350301298

Modeling the 3d world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving. However, manually creating or recreating real-world-like environments is difficult, expensive, and not scalable. Recent generative model techniques have shown promising progress to address such challenges by learning 3d assets using only plentiful 2d images - but still suffer limitations as they leverage either human-curated image datasets or renderings from manually-created synthetic 3d environments. In this paper, we introduce GINA-3d, a generative model that uses real-world driving data from camera and LidAR sensors to create realistic 3d implicit neural assets of diverse vehicles and pedestrians. Compared to the existing image datasets, the real-world driving setting poses new challenges due to occlusions, lighting-variations and long-tail distributions. GINA-3d tackles these challenges by decoupling representation learning and generative modeling into two stages with a learned tri-plane latent structure, inspired by recent advances in generative modeling of images. To evaluate our approach, we construct a large-scale object-centric dataset containing over 520K images of vehicles and pedestrians from the Waymo Open dataset, and a new set of 80K images of long-tail instances such as construction equipment, garbage trucks, and cable cars. We compare our model with existing approaches and demonstrate that it achieves state-of-the-art performance in quality and diversity for both generated images and geometries.

关键词： 3d from multi-view and sensors

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Neural Scene Chronology

Neural Scene Chronology

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lin, Haotong Wang, Qianqian Cai, Ruojin Peng, Sida Averbuch-Elor, Hadar Zhou, Xiaowei Snavely, Noah Zhejiang Univ Hangzhou Zhejiang Peoples R China Cornell Univ Ithaca NY 14853 USA Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

In this work, we aim to reconstruct a time-varying 3d model, capable of rendering photo-realistic renderings with independent control of viewpoint, illumination, and time, from Internet photos of large-scale landmarks. The core challenges are twofold. First, different types of temporal changes, such as illumination and changes to the underlying scene itself (such as replacing one graffiti artwork with another) are entangled together in the imagery. Second, scene-level temporal changes are often discrete and sporadic over time, rather than continuous. To tackle these problems, we propose a new scene representation equipped with a novel temporal step function encoding method that can model discrete scene-level content changes as piecewise constant functions over time. Specifically, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions. To facilitate our task of chronology reconstruction from Internet imagery, we also collect a new dataset of four scenes that exhibit various changes over time. We demonstrate that our method exhibits state-of-the-art view synthesis results on this dataset, while achieving independent control of viewpoint, time, and illumination. Code and data are available at https://***/NeuSC/.

关键词： 3d from multi-view and sensors

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Bi-LRFusion: Bi-directional LidAR-Radar Fusion for 3d dynamic Object detection

Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynami...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Yingjie deng, Jiajun Li, Yao Hu, Jinshui Liu, Cong Zhang, Yu Ji, Jianmin Ouyang, Wanli Zhang, Yanyong Univ Sci & Technol China Hefei Peoples R China Univ Sydney Camperdown NSW Australia 3iFLYTEK Hefei Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

LidAR and Radar are two complementary sensing approaches in that LidAR specializes in capturing an object's 3d shape while Radar provides longer detection ranges as well as velocity hints. Though seemingly natural, how to efficiently combine them for improved feature representation is still unclear. The main challenge arises from that Radar data are extremely sparse and lack height information. Therefore, directly integrating Radar features into LidAR-centric detection networks is not optimal. In this work, we introduce a bi-directional LidAR-Radar fusion framework, termed Bi-LRFusion, to tackle the challenges and improve 3d detection for dynamic objects. Technically, Bi-LRFusion involves two steps: first, it enriches Radar's local features by learning important details from the LidAR branch to alleviate the problems caused by the absence of height information and extreme sparsity;second, it combines LidAR features with the enhanced Radar features in a unified bird's-eye-view representation. We conduct extensive experiments on nuScenes and ORR datasets, and show that our Bi-LRFusion achieves state-of-the-art performance for detecting dynamic objects. Notably, Radar data in these two datasets have different formats, which demonstrates the generalizability of our method. Codes will be published.

关键词： 3d from multi-view and sensors

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Learning Adaptive dense Event Stereo from the Image domain

Learning Adaptive Dense Event Stereo from the Image Domain

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cho, Hoonhee Cho, Jegyeong Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Visual Intelligence Lab Daejeon South Korea

ISBN: (纸本)9798350301298

Recently, event-based stereo matching has been studied due to its robustness in poor light conditions. However, existing event-based stereo networks suffer severe performance degradation when domains shift. Unsupervised domain adaptation (UdA) aims at resolving this problem without using the target domain ground-truth. However, traditional UdA still needs the input event data with groundtruth in the source domain, which is more challenging and costly to obtain than image data. To tackle this issue, we propose a novel unsupervised domain Adaptive dense Event Stereo (AdES), which resolves gaps between the different domains and input modalities. The proposed AdES framework adapts event-based stereo networks from abundant image datasets with ground-truth on the source domain to event datasets without ground-truth on the target domain, which is a more practical setup. First, we propose a self-supervision module that trains the network on the target domain through image reconstruction, while an artifact prediction network trained on the source domain assists in removing intermittent artifacts in the reconstructed image. Secondly, we utilize the feature-level normalization scheme to align the extracted features along the epipolar line. Finally, we present the motion-invariant consistency module to impose the consistent output between the perturbed motion. Our experiments demonstrate that our approach achieves remarkable results in the adaptation ability of event-based stereo matching from the image domain.

关键词： 3d from multi-view and sensors

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NeRFLight: Fast and Light Neural Radiance Fields using a Shared Feature Grid

NeRFLight: Fast and Light Neural Radiance Fields using a Sha...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Rivas-Manzaneque, Fernando Sierra-Acosta, Jorge Penate-Sanchez, Adrian Moreno-Noguer, Francesc Ribeiro, Angela Arquimea Res Ctr San Cristobal la Laguna Santa Cruz De T Spain Univ Politecn Madrid Madrid Spain Univ Las Palmas Gran Canaria IUSIANI Las Palmas Gran Canaria Spain CSIC UPM Ctr Automat & Robot Madrid Spain CSIC UPC Inst Robot & Informat Ind Barcelona Spain

ISBN: (纸本)9798350301298

While original Neural Radiance Fields (NeRF) have shown impressive results in modeling the appearance of a scene with compact MLP architectures, they are not able to achieve real-time rendering. This has been recently addressed by either baking the outputs of NeRF into a data structure or arranging trainable parameters in an explicit feature grid. These strategies, however, significantly increase the memory footprint of the model which prevents their deployment on bandwidth-constrained applications. In this paper, we extend the grid-based approach to achieve real-time view synthesis at more than 150 FPS using a lightweight model. Our main contribution is a novel architecture in which the density field of NeRF-based representations is split into N regions and the density is modeled using N different decoders which reuse the same feature grid. This results in a smaller grid where each feature is located in more than one spatial position, forcing them to learn a compact representation that is valid for different parts of the scene. We further reduce the size of the final model by disposing of the features symmetrically on each region, which favors feature pruning after training while also allowing smooth gradient transitions between neighboring voxels. An exhaustive evaluation demonstrates that our method achieves real-time performance and quality metrics on a pair with state-of-the-art with an improvement of more than 2x in the FPS/MB ratio.

关键词： 3d from multi-view and sensors

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