The reconstruction of realistic and precise human meshes in world coordinates is facilitated by considering scene information. Challenges related to accuracy, robustness, and computation time are faced by existing absolute human mesh recovery methods. In this paper, a one-stage model for absolute human mesh recovery with superior reconstruction precision and inference speed is presented. The proposed one-stage model is composed of two parallel branches to achieve root position estimation and human mesh regression. To effectively connect the two branches, a scene-image information aggregation module is designed. The accuracy of the estimated human meshes is improved and the end-to-end training of the whole model is facilitated by this module. Experiments are conducted on three diverse datasets, and a GMPJPE decrease of 72.3 mm/27.32% and an MPJPE reduction of 25.6 mm/27.26% are achieved by the proposed method with the lowest inference time compared to previous SOTA methods.

Recently, learning signed distance functions (SDFs) from point clouds has become popular for reconstruction. To ensure accuracy, most methods require using high-resolution Marching Cubes for surface extraction. However, this results in redundant mesh elements, making the mesh inconvenient to use. To solve the problem, we propose an adaptive meshing method to extract resolution-adaptive meshes based on surface curvature, enabling the recovery of high-fidelity lightweight meshes. Specifically, we first use point-based representation to perceive implicit surfaces and calculate surface curvature. A vertex generator is designed to produce curvature-adaptive vertices with any specified number on the implicit surface, preserving the overall structure and high-curvature features. Then we develop a Delaunay meshing algorithm to generate meshes from vertices, ensuring geometric fidelity and correct topology. In addition, to obtain accurate SDFs for adaptive meshing and achieve better lightweight reconstruction, we design a hybrid representation combining feature grid and feature tri-plane for better detail capture. Experiments demonstrate that our method can generate high-quality lightweight meshes from point clouds. Compared with methods from various categories, our approach achieves superior results, especially in capturing more details with fewer elements.

Human mesh recovery aims to estimate all human meshes within a given image. In this article, we propose an Instance-aware Multi-person 3D Human Mesh Recovery (InstaHMR) network based on the one-stage framework. Compared to former one-stage methods, instance-aware single person feature is exploited to represent more accurate human mesh. Specifically, we propose the Contextual Instance Guidance (CIG) module which generates instance-aware single person feature by leveraging spatial and channel attention operations. In this way, it preserves more instance-specific information compared to the pixel-level feature used in some existing one-stage methods. Besides, we further introduce two auxiliary losses for better mesh recovery, namely the Human Triplet Planes (HTP) loss and the T-pose Shape (TS) loss. The HTP loss encourages the model to capture subtle differences in human joint positions, while the TS loss facilitates the learning of abstract shape parameters. By incorporating these advancements, our model achieves state-of-the-art results on four multi-person datasets.

Surface reconstruction has traditionally relied on the Multi-View Stereo (MVS)-based pipeline, which often suffers from noisy and incomplete geometry. This is due to that although MVS has been proven to be an effective way to recover the geometry of the scenes, especially for locally detailed areas with rich textures, it struggles to deal with areas with low texture and large variations of illumination where the photometric consistency is unreliable. Recently, Neural Implicit Surface Reconstruction (NISR) combines surface rendering and volume rendering techniques and bypasses the MVS as an intermediate step, which has emerged as a promising alternative to overcome the limitations of traditional pipelines. While NISR has shown impressive results on simple scenes, it remains challenging to recover delicate geometry from uncontrolled real-world scenes which is caused by its underconstrained optimization. To this end, the framework PSDF is proposed which resorts to external geometric priors from a pretrained MVS network and internal geometric priors inherent in the NISR model to facilitate high-quality neural implicit surface learning. Specifically, the visibility-aware feature consistency loss and depth prior-assisted sampling based on external geometric priors are introduced. These proposals provide powerfully geometric consistency constraints and aid in locating surface intersection points, thereby significantly improving the accuracy and delicate reconstruction of NISR. Meanwhile, the internal prior-guided importance rendering is presented to enhance the fidelity of the reconstructed surface mesh by mitigating the biased rendering issue in NISR. Extensive experiments on Tanks and Temples datasets show that PSDF achieves state-of-the-art performance on complex uncontrolled scenes.

Recently, learning multi-view neural surface reconstruction with the supervision of point clouds or depth maps has been a promising way. However, due to weak perception and underutilization of prior information, current methods still struggle with the challenges of limited accuracy and excessive time complexity. In addition, prior data perturbation is also an important yet rarely considered issue, often resulting in distorted geometry. To address these challenges, we propose a novel point-guided method named PG-NeuS, which achieves accurate and efficient reconstruction while robustly coping with point noise. Specifically, the aleatoric uncertainty of the point cloud is modeled to capture the noise distribution, estimating the reliability of each point and enhancing robustness against noise. Moreover, a Neural Projection module is proposed to connect points and images, adding geometric constraints to the implicit surface and achieving more precise point guidance. To better compensate for geometric bias between volume rendering and point modeling, we additionally design a Bias network that leverages the geometric information in high-fidelity points to enhance detail representation. Benefiting from the effective point guidance, the proposed PG-NeuS achieves an 11x speed increase and a 33.3% accuracy improvement compared to NeuS on DTU, even with a lightweight network. Extensive experiments show that our method yields high-quality surfaces with high efficiency, especially for fine-grained details and smooth regions, outperforming the state-of-the-art methods. Moreover, it exhibits strong robustness to noisy data and sparse data.

Recently, there has been a growing interest in 3D CNN-based stereo matching methods due to their remarkable accuracy. However, the high complexity of 3D convolution makes it challenging to strike a balance between accuracy and speed. Notably, explicit 3D volumes contain considerable redundancy. In this study, we delve into more compact 2D implicit network to eliminate redundancy and boost real-time performance. However, simply replacing explicit 3D networks with 2D implicit networks causes issues that can lead to performance degradation, including the loss of structural information, the quality decline of inter-image information, as well as the inaccurate regression caused by low-level features. To address these issues, we first integrate intra-image information to fuse with inter-image information, facilitating propagation guided by structural cues. Subsequently, we introduce the Fast Multi-scale Score Volume (FMSV) and Confidence Based Filtering (CBF) to efficiently acquire accurate multi-scale, noise-free inter-image information. Furthermore, combined with the Residual Context-aware Upsampler (RCU), our Intra-Inter Fusing network is meticulously designed to enhance information transmission on both feature-level and disparity-level, thereby enabling accurate and robust regression. Experimental results affirm the superiority of our network in terms of both speed and accuracy compared to all other fast methods.

Recently, there has been a growing interest in learningbased explicit methods due to their ability to respect the original input and preserve details. However, the connectivity on complex structures is still difficult to infer due to the limited local shape perception, resulting in artifacts and non-watertight triangles. In this paper, we present a novel learning-based method with Delaunay triangulation to achieve high-precision reconstruction. We model the Delaunay triangulation as a dual graph, extract local geometric information from the points, and embed it into the structural representation of Delaunay triangulation in an organic way, benefiting fine-grained details reconstruction. To encourage neighborhood information interaction of edges and nodes in the graph, we introduce a local graph iteration algorithm, which is a variant of graph neural network. Moreover, a geometric constraint loss further improves the classification of tetrahedrons. Benefiting from our fully local network, a scaling strategy is designed to enable large-scale reconstruction. Experiments show that our method yields watertight and high-quality meshes. Especially for some thin structures and sharp edges, our method shows better performance than the current state-of-the-art methods. Furthermore, it has a strong adaptability to point clouds of different densities.