Tengping Jiang

• Doctor of Philosophy
• associate professor at Nanjing Normal University

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value, but its development is severely hindered by the lack of suitable and specific datasets. Additionally, the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large do...

Automatic and accurate instance segmentation of street trees from point clouds is a fundamental task in urban green space research. Previous studies have achieved satisfactory tree segmentation results in simple scenarios. However, for challenging cases, including adjacent overlapping tree crowns, irregular tree shapes, and incompleteness caused by...

The accurate leaf-wood separation of individual trees from point clouds is an important yet challenging task. Many existing methods rely on manual features that are time-consuming and labor-intensive to distinguish between leaf and wood points. However, due to the complex interlocking structure of leaves and wood in the canopy, these methods have n...

The representation quantifies the geometric shape and topology of a building is a necessary procedure for many urban planning applications. A sharp line framework is a high-level structural cue providing a compact building representation. However, accurate and efficient structural line extraction remains a challenging task given the variety and com...

In this paper, we propose an embarrassingly simple yet highly effective zero-shot semantic segmentation (ZS3) method, based on the pre-trained vision-language model CLIP. First, our study provides a couple of key discoveries: (i) the global tokens (a.k.a [CLS] tokens in Transformer) of the text branch in CLIP provide a powerful representation of se...

As one of the most important components of urban space, an outdated inventory of road-side trees may misguide managers in the assessment and upgrade of urban environments, potentially affecting urban road quality. Therefore, automatic and accurate instance segmentation of road-side trees from urban point clouds is an important task in urban ecology...

To quantify the architecture and select the ideal ideotype, it is vital to accurately measure the dimension of each part of the mantis shrimp. Point clouds have become increasingly popular in recent years as an efficient solution. However, the current manual measurement is labor intensive and costly and has high uncertainty. Automatic organ point c...

Incomplete or outdated inventories of railway infrastructures may disrupt the railway sector’s administration and maintenance of transportation infrastructure, thus posing potential threats to the safety of traffic networks. Previous studies have adopted point clouds to accelerate inventory and inspection automation procedures. However, owing to th...

Automatically representing the semantics and topology of indoor building spaces from floor-plans is necessary for many applications, such as architectural design and indoor renovations. Extensive studies have investigated reconstructing indoor spaces with semantics and topology using professional means (e.g., laser scanning and photogrammetry). Flo...

Interpretation of airborne laser scanning (ALS) point clouds plays a notable role in geoinformation production. As a critical step for interpretation, accurate semantic segmentation can considerably broaden various applications of ALS data. However, most existing methods cannot provide precise annotations and high robustness due to occlusions, vari...

Although many notable improvements have been devoted to the semantic segmentation of laser scanning (LS) data, the extreme complexity of scanned scenes poses significant challenges in achieving the effective distribution of a category label per point. This study investigates the semantic segmentation of LiDAR point clouds using an improved deep lea...

Objective Point cloud classification is one of the hotspots of computer vision research. Among of various kinds of processing stages, accurately describing the local neighborhood structure of the point cloud and extracting the point cloud feature sets with strong expressive ability has become the key to point cloud classification. Traditionally, tw...

This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex r...

Accurate highway alignments and three-dimensional (3D) models are essential for various intelligent transportation applications. Airborne laser scanning (ALS) provides a desirable means of data collection, which increases data quality and collection efficiency. However, automatic alignments extraction and 3D modeling remain open problems. Therefore...

The urban heat island (UHI) effect in cities and its driving factors have long been investigated. 3D buildings are key components of urban structures and have notable effect on UHI effect. However, due to the incomplete 3D building information in urban database, only a few studies investigated the impact of 3D building morphology factors on the lan...

The result of point cloud semantic segmentation includes the recognition of multiple objects in the scene, which is an important part of 3D scene information extraction. It also plays a key role in many fields such as smart cities. Since the large amount of data and high scene complexity, however, most existing methods can only extract a limited ty...

In large-scale road environment, point-based methods require dynamic calculations, and voxel-based methods often lose a lot of information when balancing resolution and performance. To overcome the drawbacks of the above two classical methods, this paper proposes a general network architecture that combines bi-level convolution and dynamic graph ed...

Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in comple...

Pole-like objects are commonly occurring features on roads, and their identification in photographs is essential to the management and mapping of road information. In particular, mobile laser scanning systems comprise one of the most accurate and efficient techniques to gather road-related geospatial information. The automatic detection and classif...

The extraction of buildings has been an essential part of the field of LiDAR point clouds processing in recent years. However, it is still challenging to extract buildings from huge amount of point clouds due to the complicated and incomplete structures, occlusions and local similarities between different categories in a complex environment. Taking...

Nowadays 3D scene labeling has become a hot topic in the reform of machine learning,photogrammerty,computer vision,etc. Road facility semantic labelling is vital for large scale mapping and autonomous driving systems. This paper proposes a new method for semantic tagging of road facilities based on logical relationships and functionality. We first...