Pose-Free Neural Radiance Fields via Implicit Pose Regularization

Pose-free neural radiance fields (NeRF) aim to train NeRF with unposed multi-view images and it has achieved very impressive success in recent years. Most existing works share the pipeline of training a coarse pose estimator with rendered images at first, followed by a joint optimization of estimated poses and neural radiance field. However, as the pose estimator is trained with only rendered images, the pose estimation is usually biased or inaccurate for real images due to the domain gap between real images and rendered images, leading to poor robustness for the pose estimation of real images and further local minima in joint optimization. We design IR-NeRF, an innovative pose-free NeRF that introduces implicit pose regularization to refine pose estimator with unposed real images and improve the robustness of the pose estimation for real images. With a collection of 2D images of a specific scene, IR-NeRF constructs a scene codebook that stores scene features and captures the scene-specific pose distribution implicitly as priors. Thus, the robustness of pose estimation can be promoted with the scene priors according to the rationale that a 2D real image can be well reconstructed from the scene codebook only when its estimated pose lies within the pose distribution. Extensive experiments show that IR-NeRF achieves superior novel view synthesis and outperforms the state-of-the-art consistently across multiple synthetic and real datasets.Comment: Accepted by ICCV202

Weakly Supervised 3D Open-vocabulary Segmentation

Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research. However, this task is heavily impeded by the lack of large-scale and diverse 3D open-vocabulary segmentation datasets for training robust and generalizable models. Distilling knowledge from pre-trained 2D open-vocabulary segmentation models helps but it compromises the open-vocabulary feature as the 2D models are mostly finetuned with close-vocabulary datasets. We tackle the challenges in 3D open-vocabulary segmentation by exploiting pre-trained foundation models CLIP and DINO in a weakly supervised manner. Specifically, given only the open-vocabulary text descriptions of the objects in a scene, we distill the open-vocabulary multimodal knowledge and object reasoning capability of CLIP and DINO into a neural radiance field (NeRF), which effectively lifts 2D features into view-consistent 3D segmentation. A notable aspect of our approach is that it does not require any manual segmentation annotations for either the foundation models or the distillation process. Extensive experiments show that our method even outperforms fully supervised models trained with segmentation annotations in certain scenes, suggesting that 3D open-vocabulary segmentation can be effectively learned from 2D images and text-image pairs. Code is available at \url{https://github.com/Kunhao-Liu/3D-OVS}.Comment: Accepted to NeurIPS 202

Photosynthesis-assisted remodeling of three-dimensional printed structures

The mechanical properties of engineering structures continuously weaken during service life because of material fatigue or degradation. By contrast, living organisms are able to strengthen their mechanical properties by regenerating parts of their structures. For example, plants strengthen their cell structures by transforming photosynthesis-produced glucose into stiff polysaccharides. In this work, we realize hybrid materials that use photosynthesis of embedded chloroplasts to remodel their microstructures. These materials can be used to three-dimensionally (3D)-print functional structures, which are endowed with matrix-strengthening and crack healing when exposed to white light. The mechanism relies on a 3D-printable polymer that allows for an additional cross-linking reaction with photosynthesis-produced glucose in the material bulk or on the interface. The remodeling behavior can be suspended by freezing chloroplasts, regulated by mechanical preloads, and reversed by environmental cues. This work opens the door for the design of hybrid synthetic-living materials, for applications such as smart composites, lightweight structures, and soft robotics

Tough and Self-Healable Nanocomposite Hydrogels for Repeatable Water Treatment

Nanomaterials with ultrahigh specific surface areas are promising adsorbents for water-pollutants such as dyes and heavy metal ions. However, an ongoing challenge is that the dispersed nanomaterials can easily flow into the water stream and induce secondary pollution. To address this challenge, we employed nanomaterials to bridge hydrogel networks to form a nanocomposite hydrogel as an alternative water-pollutant adsorbent. While most of the existing hydrogels that are used to treat wastewater are weak and non-healable, we present a tough TiO2 nanocomposite hydrogel that can be activated by ultraviolet (UV) light to demonstrate highly efficient self-healing, heavy metal adsorption, and repeatable dye degradation. The high toughness of the nanocomposite hydrogel is induced by the sequential detachment of polymer chains from the nanoparticle crosslinkers to dissipate the stored strain energy within the polymer network. The self-healing behavior is enabled by the UV-assisted rebinding of the reversible bonds between the polymer chains and nanoparticle surfaces. Also, the UV-induced free radicals on the TiO2 nanoparticle can facilitate the binding of heavy metal ions and repeated degradation of dye molecules. We expect this self-healable, photo-responsive, tough hydrogel to open various avenues for resilient and reusable wastewater treatment materials

An ensemble method utilising multiple thinking styles that boosts the wisdom of the inner crowd effect

Previous studies have demonstrated that individuals can utilize the wisdom of crowds, known as ‘the wisdom of the inner crowd’. This requires them to estimate a single question multiple times, and subsequently average these estimates. Although several methods have been proposed to achieve more accurate estimates, its efficacy remains relatively low. Therefore, this study proposes a method that assembles four independent methods to stimulate the wisdom of the inner crowd effect. In particular, our method instructs participants to provide estimates five times:1) making an estimate intuitively, 2) making an estimate deliberately, 3) considering the opposite (i.e. dialectical bootstrapping), 4) taking the general crowd’s perspective, and 5) taking the disagreeing other’s perspective. Through a behavioural experiment, we confirmed that our method can produce the wisdom of the inner crowd effect. Moreover, we found that our method produced more accurate estimates than a method that required participants to estimate five times without specific instructions. Furthermore, mathematical modelling demonstrated that the effectiveness of our method was greater than that of 1.5 persons. In sum, this study proposes a method to improve daily estimates

A multi-strategy contrastive learning framework for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) has gained significant popularity as it relies only on weak labels such as image level annotations rather than the pixel level annotations required by supervised semantic segmentation (SSS) methods. Despite drastically reduced annotation costs, typical feature representations learned from WSSS are only representative of some salient parts of objects and less reliable compared to SSS due to the weak guidance during training. In this paper, we propose a novel Multi-Strategy Contrastive Learning (MuSCLe) framework to obtain enhanced feature representations and improve WSSS performance by exploiting similarity and dissimilarity of contrastive sample pairs at image, region, pixel and object boundary levels. Extensive experiments demonstrate the effectiveness of our method and show that MuSCLe outperforms current state-of-the-art methods on the widely used PASCAL VOC 2012 dataset

Sharkskin-Inspired Magnetoactive Reconfigurable Acoustic Metamaterials

Most of the existing acoustic metamaterials rely on architected structures with fixed configurations, and thus, their properties cannot be modulated once the structures are fabricated. Emerging active acoustic metamaterials highlight a promising opportunity to on-demand switch property states; however, they typically require tethered loads, such as mechanical compression or pneumatic actuation. Using untethered physical stimuli to actively switch property states of acoustic metamaterials remains largely unexplored. Here, inspired by the sharkskin denticles, we present a class of active acoustic metamaterials whose configurations can be on-demand switched via untethered magnetic fields, thus enabling active switching of acoustic transmission, wave guiding, logic operation, and reciprocity. The key mechanism relies on magnetically deformable Mie resonator pillar (MRP) arrays that can be tuned between vertical and bent states corresponding to the acoustic forbidding and conducting, respectively. The MRPs are made of a magnetoactive elastomer and feature wavy air channels to enable an artificial Mie resonance within a designed frequency regime. The Mie resonance induces an acoustic bandgap, which is closed when pillars are selectively bent by a sufficiently large magnetic field. These magnetoactive MRPs are further harnessed to design stimuli-controlled reconfigurable acoustic switches, logic gates, and diodes. Capable of creating the first generation of untethered-stimuli-induced active acoustic metadevices, the present paradigm may find broad engineering applications, ranging from noise control and audio modulation to sonic camouflage

Effects of climate warming and human activities on the distribution patterns of Fritillaria unibracteata in eastern Qinghai-Tibetan Plateau

Abstract Fritillaria unibracteata is an endangered medicinal material species endemic to the Qinghai Tibet Plateau, and belongs to the national Class III endangered plant. In addition to expelling wind and removing damne, it also warms menstruation and relieves pain in clinic use of tranditional Chinese medicine. In recent years, affected by the destruction of shrubs and climate change, the habitat of F. unibracteata wild resources has been seriously damaged, indicating of great significance to predict its potential suitable habitat using MaxEnt model. The AUC values without human activities were 0.983 ± 0.013–0.988 ± 0.001, while it is 0.982 ± 0.015–0.989 ± 0.000 with human activities, justifying their applications for predicting the potential areas of F. unibracteata. Without human activities, there were 8.47 × 104 km2 of highly suitable habitats in northern Sichuan, southern Gansu and southeastern Qinghai. But the poorly, moderately and highly suitable areas of F. unibracteata have decreased to 33.8 × 104 km2, 9.66 × 104 km2 and 6.64 × 104 km2 due to human activities. Environmental variables affecting F. unibracteata distribution included the minimum temperature in the coldest month (−16.89–−4.96 °C), annual precipitation (416.64–866.96 mm), temperature annual range (24.83–31.97 °C), elevation (2879.69–3981.82 m), human footprint (2.58–23.66) and mean UV-B of highest month (7381.92–8574.27 kJ/m2). In the 2050s and 2090s, human activities would significantly reduce the highly suitable habitats of F. unibracteata. Under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, the centroid would move to the low latitude area from the current position first, and then to a high latitude area. Wild resources of F. unibracteata in China can be effectively conserved based on our results

The Application of Tomato Plant Residue Compost and Plant Growth-Promoting Rhizobacteria Improves Soil Quality and Enhances the Ginger Field Soil Bacterial Community

Treating and utilizing vegetable residues may reduce waste and improve rhizosphere soil. This study explored the effects of tomato plant residue compost and plant growth-promoting rhizobacteria (PGPR) on the physicochemical properties and microbial community of ginger field soil. Four treatment procedures were adopted: no compost or PGPR (CK), compost (TC), compost + Bacillus subtilis (TC-BS), and compost +Bacillus amyloliquefaciens SQR9 (TC-BA). The results showed that compared with the CK, TC significantly increased soil organic matter, alkali hydrolyzable nitrogen, available phosphorus, and available potassium by 17.34%, 21.66%, 19.56%, and 37.20%, respectively. Soil urease activity, neutral phosphatase activity, and sucrase activity increased by 55.89%, 35.59%, and 57.21%, respectively. Chloroflexi, Gemmatimonadetes, and Bacillus abundances increased by 1.40%, 1.80%, and 0.68%, respectively, while Firmicutes decreased by 0.80%. TC-BS significantly improved soil bacterial diversity than CK and TC, and relative abundance of Beneficial Proteobacteria, Acidobacteria, Chloroflexi, and Bacillus microorganisms dominated. Principal coordinate analysis revealed significant differences in bacterial community structure among different treatments. Redundancy analysis indicated total potassium (p = 0.002), pH (p = 0.0012), and available phosphorus (p = 0.016) as the main community composition driving factors. In conclusion, B. subtilis inoculation in ginger field soil supplemented with tomato compost enhanced bacterial diversity, altered bacterial community structure, enriched beneficial microorganisms, and promoted a healthy rhizosphere