Monocular scene understanding is a foundational component of autonomous systems. Within the spectrum of monocular perception topics, one crucial and useful task for holistic 3D scene understanding is semantic scene completion (SSC), which jointly completes semantic information and geometric details from RGB input. However, progress in SSC, particularly in large-scale street views, is hindered by the scarcity of high-quality datasets. To address this issue, we introduce SSCBench, a comprehensive benchmark that integrates scenes from widely used automotive datasets (e.g., KITTI-360, nuScenes, and Waymo). SSCBench follows an established setup and format in the community, facilitating the easy exploration of SSC methods in various street views. We benchmark models using monocular, trinocular, and point cloud input to assess the performance gap resulting from sensor coverage and modality. Moreover, we have unified semantic labels across diverse datasets to simplify cross-domain generalization testing. We commit to including more datasets and SSC models to drive further advancements in this field.
Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University（车辆先进设计与制造技术国家重点实验室）
VOIC: Visible-Occluded Integrated Guidance for 3D Semantic Scene Completion
Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（可信自主系统研究院，南方科技大学）；Department of Computer Science and Engineering, Southern University of Science and Technology（计算机科学与工程系，南方科技大学）；School of Computer Science, University of Nottingham Ningbo China（宁波大学计算机学院）；Department of Electronic and Information Engineering, Changchun University（电子与信息工程学院，长春大学）
One Step Closer: Creating the Future to Boost Monocular Semantic Scene Completion
School of Computer Science and Technology（计算机科学与技术学院）；Xi'an Jiaotong University（西安交通大学）；The Institute of Computing（计算研究所）；China Academy of Railway Sciences Corporation（中国铁路科学研究院）
A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（香港理工大学电机工程学系）；School of Mechanical Engineering, Hubei University of Technology（湖北工业大学机械工程学院）
Targeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving
MoE Key Lab of Artificial Intelligence, Institute of AI, Shanghai Jiao Tong University（教育部人工智能重点实验室，上海交通大学人工智能研究院）；Central Research Institute, Huawei（华为中央研究院）
University of Stuttgart（斯图加特大学）；Robert Bosch GmbH（罗伯特·博世有限公司）
Predicting future 3D LiDAR pointclouds is a challenging task that is useful in many applications in autonomous driving such as trajectory prediction, pose forecasting and decision making. In this work, we propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs), titled Stochastic LiDAR Prediction and Completion (SLPC). Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. Our contributions can thu…
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving
School of Instrument Science and Engineering, Southeast University（仪器科学与工程学院，东南大学）；School of Geospatial Engineering and Science, Sun Yat-sen University（地理信息工程与科学学院，中山大学）
Mannheim University of Applied Sciences（曼海姆应用科学大学）
Depth completion from sparse LiDAR and high-resolution RGB data is one of the foundations for autonomous driving techniques. Current approaches often rely on CNN-based methods with several known drawbacks: flying pixel at depth discontinuities, overfitting to both a given data set as well as error metric, and many more. Thus, we propose our novel Piecewise Depth Completion (PDC), which works completely without deep learning. PDC segments the RGB image into superpixels corresponding the regions with similar depth value. Superpixels corresponding to same objects are gathered using a cost map. At…
Depth completion has attracted extensive attention recently due to the development of autonomous driving, which aims to recover dense depth map from sparse depth measurements. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art methods in this task, which adopt a linear propagation model to refine coarse depth maps with local context. However, the propagation of each pixel occurs in a fixed receptive field. This may not be the optimal for refinement since different pixel needs different local context. To tackle this issue, in this paper, we propose a deformable spat…
SenseTime Research（商汤科技研究院）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）
Depth completion aims to recover dense depth maps from sparse depth measurements. It is of increasing importance for autonomous driving and draws increasing attention from the vision community. Most of existing methods directly train a network to learn a mapping from sparse depth inputs to dense depth maps, which has difficulties in utilizing the 3D geometric constraints and handling the practical sensor noises. In this paper, to regularize the depth completion and improve the robustness against noise, we propose a unified CNN framework that 1) models the geometric constraints between depth an…
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；SenseTime Research（商汤科技研究院）；Chinese University of Hong Kong（香港中文大学）
Dense depth cues are important and have wide applications in various computer vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth measurements around the vehicle to perceive the surrounding environments. However, depth maps obtained by LIDAR are generally sparse because of its hardware limitation. The task of depth completion attracts increasing attention, which aims at generating a dense depth map from an input sparse depth map. To effectively utilize multi-scale features, we propose three novel sparsity-invariant operations, based on which, a sparsity-invariant mu…
DSOcc: Leveraging Depth Awareness and Semantic Aid to Boost Camera-Based 3D Semantic Occupancy Prediction
S-Lab & College of Computing and Data Science, Nanyang Technological University（S实验室及计算与数据科学学院，南洋理工大学）；MMLab, The Chinese University of Hong Kong（M实验室，香港中文大学）；State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University（流体动力与机电系统国家重点实验室，浙江大学）；SenseTime Research, Hong Kong（商汤研究，香港）
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Samsung Research（三星研究院）；South China Normal University（华南师范大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrated and dominated by deep learning based solutions. In this article, for the first time, we provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design…
Tsinghua University（清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学智能产业研究院（AIR））；University of Chinese Academy of Sciences（中国科学院大学）
Scene completion refers to obtaining dense scene representation from an incomplete perception of complex 3D scenes. This helps robots detect multi-scale obstacles and analyse object occlusions in scenarios such as autonomous driving. Recent advances show that implicit representation learning can be leveraged for continuous scene completion and achieved through physical constraints like Eikonal equations. However, former Eikonal completion methods only demonstrate results on watertight meshes at a scale of tens of meshes. None of them are successfully done for non-watertight LiDAR point clouds …
Sparse depth measurements are widely available in many applications such as augmented reality, visual inertial odometry and robots equipped with low cost depth sensors. Although such sparse depth samples work well for certain applications like motion tracking, a complete depth map is usually preferred for broader applications, such as 3D object recognition, 3D reconstruction and autonomous driving. Despite the recent advancements in depth prediction from single RGB images with deeper neural networks, the existing approaches do not yield reliable results for practical use. In this work, we prop…
Simon Fraser University（西蒙弗雷泽大学）；National University of Defense Technology（国防科技大学）；Tianjin University（天津大学）；State Administration of Cultural Heritage（国家文物局）
Dense depth perception is critical for autonomous driving and other robotics applications. However, modern LiDAR sensors only provide sparse depth measurement. It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion. Many neural networks have been designed for this task. However, they often na\"ıvely fuse the LiDAR data and RGB image information by performing feature concatenation or element-wise addition. Inspired by the guided image filtering, we design a novel guided network to predict kernel weights from th…
3D Semantic Scene Completion (SSC) can provide dense geometric and semantic scene representations, which can be applied in the field of autonomous driving and robotic systems. It is challenging to estimate the complete geometry and semantics of a scene solely from visual images, and accurate depth information is crucial for restoring 3D geometry. In this paper, we propose the first stereo SSC method named OccDepth, which fully exploits implicit depth information from stereo images (or RGBD images) to help the recovery of 3D geometric structures. The Stereo Soft Feature Assignment (Stereo-SFA) …
Zhejiang University（浙江大学）；University of Glasgow（格拉斯哥大学）
LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small and distant objects due to insufficient laser points, while the robust multi-modal solution is under-explored, where we investigate three crucial inherent difficulties: modality heterogeneity, limited sensor field of view intersection, and multi-modal data augmentation. We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigat…
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Nanjing University of Science and Technology（南京理工大学）；Huazhong University of Science and Technology（华中科技大学）；Chinese Academy of Sciences（中国科学院）；Northwestern Polytechnical University（西北工业大学）
Zhejiang University（浙江大学）；Hunan University（湖南大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Shanghai SUPREMIND Technology Company Ltd.（上海思普睿明科技有限公司）
CurriFlow: Curriculum-Guided Depth Fusion with Optical Flow-Based Temporal Alignment for 3D Semantic Scene Completion
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Ministry of Education（教育部）；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算机科学中心（济南国家超算中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing（山东省计算电力互联网与服务计算重点实验室）；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）
L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学网络系统与控制研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Zhejiang Guoli Security Technology Co., Ltd.（浙江国利安全科技有限公司）
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Deptrum Ltd.（深度限公司）
Image guided depth completion aims to recover per-pixel dense depth maps from sparse depth measurements with the help of aligned color images, which has a wide range of applications from robotics to autonomous driving. However, the 3D nature of sparse-to-dense depth completion has not been fully explored by previous methods. In this work, we propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. First, unlike previous methods, we leverage convolution neural networks as well as graph neural networks in a complementary way for geomet…
TACO: Taming Diffusion for in-the-wild Video Amodal Completion
State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；State Key Laboratory of General Artificial Intelligence, BIGAI（BIGAI 通用人工智能国家重点实验室）；Tsinghua University（清华大学）
