Existing neural radiance fields (NeRF)-based novel view synthesis methods for large-scale outdoor scenes are mainly built on a single altitude. Moreover, they often require a priori camera shooting height and scene scope, leading to inefficient ...

Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone. While data augmentation is an effective technique to enrich the training data, existing methods for camera data in autonomous ...

As NeRFs modeling becomes more widely available, there is an increasing demand for the ability to flexibly and conveniently exclude unnecessary obstructions during the modeling process. Existing methods generally adopt a “ignore” strategy for ...

Few-shot 3D scene reconstruction remains a challenge due to the limited number of viewpoints available for rendering high-quality images. The proposed framework, SFDNeRF, addresses this by integrating semantic feature-driven constraints and hybrid ...

Rendering animatable avatars from monocular videos has significant applications in the broader field of interactive entertainment. Previous methods based on Neural Radiance Field (NeRF) struggle with long training time and tend to overfit on seen ...

For the shortcomings of current 3D reconstruction models such as poor reconstruction effect and blurred edges when dealing with weakly textured and textureless objects, this paper fuses the rich polarization spectral information with multi-view 3D ...

The utilization of the triplane-based radiance fields has gained attention in recent years due to its ability to effectively disentangle 3D scenes with a high-quality representation and low computation cost. A key requirement of this method is the ...

Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose ...

Novel view synthesis via Neural Radiance Fields (NeRFs) or 3D Gaussian Splatting (3DGS) typically necessitates dense observations with hundreds of input images to circumvent artifacts. We introduce Deceptive-NeRF/3DGS (In harmonic progression, a ...

While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches demonstrate proficiency in transferring colors and textures but often ...

Existing NeRF-based instance segmentation methods lift 2D annotated or predicted semantic and instance masks into 3D through radiance field, but still insufficient in segmenting on the unlabeled semantic classes. The Segment Anything Model (SAM) ...

Representing the world in 3D space provides vivid texture and depth information. However, 3D datasets currently do not match the scale of 2D datasets. There is a growing trend in representing 3D data as multi-view 2D images and using large-scale ...

The primary aim of this study is to enhance the quality of synthetic novel views under conditions characterized by data sparsity. Our investigations demonstrate that the utilization of multi-scale, multi-level stereoscopic depth feature extraction ...

Neural Radiance Fields (NeRF) is an emerging technique to synthesize 3D objects from 2D images with a wide range of potential applications. However, rendering existing NeRF models is extremely computation intensive, making it challenging to support real-...

We present a method for estimating the intrinsic components of a dynamic scene captured with multi-view video sequences. Unlike previous work focused either on static scenes or single view videos, our method simultaneously addresses the ...

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• Novel view synthesis and inverse rendering of dynamic (moving) is an open problem.
• We leverage a temporal embedding to represent the dynamism in the scene.
• We produce albedo, specular, visibility, and normals for a novel view of a ...

We delve into the physics-informed neural reconstruction of smoke and obstacles through sparse-view RGB videos, tackling challenges arising from limited observation of complex dynamics. Existing physics-informed neural networks often emphasize short-...

Traditional 3D face models are based on mesh representations with texture. One of the most important models is Flame (Faces Learned with an Articulated Model and Expressions), which produces meshes of human faces that are fully controllable. ...

Collaborating around physical objects necessitates examining different aspects of design or hardware in detail when reviewing or inspecting physical artifacts or prototypes. When collaborators are remote, coordinating the sharing of views of their ...

Accurate reconstruction of plant phenotypes plays a key role in optimizing sustainable farming practices in the field of Precision Agriculture (PA). Currently, optical sensor-based approaches dominate the field, but the need for high-fidelity 3D ...

• Explore NeRF in high-fidelity plant phenotyping.
• Introduction of a first-of-its-kind initiative dataset for plant phenotyping.
• Comprehensive study of several SOTA NeRF models.

Neural Radiance Fields (NeRF) has recently shown impressive results in synthesizing novel views of complex scenes from images. However, its performance is highly dependent on the quality of the estimated pose. Although existing methods have achieved ...