CVPR 2024 Papers

Can we capture shape and reflectance in stealth? Such capability would be valuable for many application domains in vision xR robotics and HCI. We introduce structured polarization for invisible depth and reflectance sensing (SPIDeRS) the first depth and reflectance sensing method using patterns of polarized light. The key idea is to modulate the angle of linear polarization (AoLP) of projected light at each pixel. The use of polarization makes it invisible and lets us recover not only depth but also directly surface normals and even reflectance. We implement SPIDeRS with a liquid crystal spatial light modulator (SLM) and a polarimetric camera. We derive a novel method for robustly extracting the projected structured polarization pattern from the polarimetric object appearance. We evaluate the effectiveness of SPIDeRS by applying it to a number of real-world objects. The results show that our method successfully reconstructs object shapes of various materials and is robust to diffuse reflection and ambient light. We also demonstrate relighting using recovered surface normals and reflectance. We believe SPIDeRS opens a new avenue of polarization use in visual sensing.

Compressive spectral image reconstruction is a critical method for acquiring images with high spatial and spectral resolution. Current advanced methods which involve designing deeper networks or adding more self-attention modules are limited by the scope of attention modules and the irrelevance of attentions across different dimensions. This leads to difficulties in capturing non-local mutation features in the spatial-spectral domain and results in a significant parameter increase but only limited performance improvement. To address these issues we propose SPECAT a SPatial-spEctral Cumulative-Attention Transformer designed for high-resolution hyperspectral image reconstruction. SPECAT utilizes Cumulative-Attention Blocks (CABs) within an efficient hierarchical framework to extract features from non-local spatial-spectral details. Furthermore it employs a projection-object Dual-domain Loss Function (DLF) to integrate the optical path constraint a physical aspect often overlooked in current methodologies. Ultimately SPECAT not only significantly enhances the reconstruction quality of spectral details but also breaks through the bottleneck of mutual restriction between the cost and accuracy in existing algorithms. Our experimental results demonstrate the superiority of SPECAT achieving 40.3 dB in hyperspectral reconstruction benchmarks outperforming the state-of-the-art (SOTA) algorithms by 1.2 dB while using only 5% of the network parameters and 10% of the computational cost. The code is available at https://github.com/THU-luvision/SPECAT.

We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions and fine-tune it on a high quality subset of Objaverse. We find that a naive extension of the self-attention proposed in prior work (e.g. MVDream) leads to content copying between views. Therefore we explicitly constrain the cross-view attention based on epipolar geometry. To further enhance 3D consistency we utilize Pl ?ucker coordinates derived from camera rays and inject them as positional encoding. This enables SPAD to reason over spatial proximity in 3D well. Compared to concurrent works that can only generate views at fixed azimuth and elevation (e.g. MVDream SyncDreamer) SPAD offers full camera control and achieves state-of-the-art results in novel view synthesis on unseen objects from the Objaverse and Google Scanned Objects datasets. Finally we demonstrate that text-to-3D generation using SPAD prevents the multi-face Janus issue.

Reasoning from visual dynamics scenes has many real world applications. However existing video reasoning benchmarks are still inadequate since they were mainly designed for factual or situated reasoning and rarely involve broader knowledge in the real world. Our work aims to delve deeper into reasoning evaluations specifically within dynamic open-world and structured context knowledge. We propose a new benchmark (SOK-Bench) consisting of 44K questions and 10K situations with instance-level annotations depicted in the videos. The reasoning process is required to understand and apply situated knowledge and general knowledge for problem-solving. To create such a dataset we propose an automatic and scalable generation method to generate question-answer pairs knowledge graphs and rationales by instructing the combinations of LLMs and MLLMs. Concretely we first extract observable situated entities relations and processes from videos for situated knowledge and then extend to open-world knowledge beyond the visible content. The task generation is facilitated through multiple dialogues as iterations and subsequently corrected and refined by our designed self-promptings and demonstrations. With a corpus of both explicit situated facts and implicit commonsense we generate associated question-answer pairs and reasoning processes finally followed by manual reviews for quality assurance. We evaluated recent mainstream large vision language models on the benchmark and found several insightful conclusions. For more information please refer to our benchmark at www.bobbywu.com/SOKBench.

We introduce SODA a self-supervised diffusion model designed for representation learning. The model incorporates an image encoder which distills a source view into a compact representation that in turn guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder and leveraging novel view synthesis as a self-supervised objective we can turn diffusion models into strong representation learners capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge SODA is the first diffusion model to succeed at ImageNet linear-probe classification and at the same time it accomplishes reconstruction editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space that serves as an effective interface to control and manipulate the produced images. All in all we aim to shed light on the exciting and promising potential of diffusion models not only for image generation but also for learning rich and robust representations. See our website at soda-diffusion.github.io.

In rapidly-evolving domains such as autonomous driving the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To correctly exploit the provided information by each sensor in a single common frame it is essential for these sensors to be accurately calibrated. In this paper we leverage the ability of Neural Radiance Fields (NeRF) to represent different sensors modalities in a common volumetric representation to achieve robust and accurate spatio-temporal sensor calibration. By designing a partitioning approach based on the visible part of the scene for each sensor we formulate the calibration problem using only the overlapping areas. This strategy results in a more robust and accurate calibration that is less prone to failure. We demonstrate that our approach works on outdoor urban scenes by validating it on multiple established driving datasets. Results show that our method is able to get better accuracy and robustness compared to existing methods.

Misinformation is a prevalent societal issue due to its potential high risks. Out-Of-Context (OOC) misinformation where authentic images are repurposed with false text is one of the easiest and most effective ways to mislead audiences. Current methods focus on assessing image-text consistency but lack convincing explanations for their judgments which are essential for debunking misinformation. While Multimodal Large Language Models (MLLMs) have rich knowledge and innate capability for visual reasoning and explanation generation they still lack sophistication in understanding and discovering the subtle cross-modal differences. In this paper we introduce Sniffer a novel multimodal large language model specifically engineered for OOC misinformation detection and explanation. Sniffer employs two-stage instruction tuning on InstructBLIP. The first stage refines the model's concept alignment of generic objects with news-domain entities and the second stage leverages OOC-specific instruction data generated by language-only GPT-4 to fine-tune the model's discriminatory powers. Enhanced by external tools and retrieval Sniffer not only detects inconsistencies between text and image but also utilizes external knowledge for contextual verification. Our experiments show that Sniffer surpasses the original MLLM by over 40% and outperforms state-of-the-art methods in detection accuracy. Sniffer also provides accurate and persuasive explanations as validated by quantitative and human evaluations.

Once only a few-shot annotated samples are available the performance of learning-based object detection would be heavily dropped. Many few-shot object detection (FSOD) methods have been proposed to tackle this issue by adopting image-level augmentations in linear manners. Nevertheless those handcrafted enhancements often suffer from limited diversity and lack of semantic awareness resulting in unsatisfactory performance. To this end we propose a Semantic-guided Non-linear Instance-level Data Augmentation method (SNIDA) for FSOD by decoupling the foreground and background to increase their diversities respectively. We design a semantic awareness enhancement strategy to separate objects from backgrounds. Concretely masks of instances are extracted by an unsupervised semantic segmentation module. Then the diversity of samples would be improved by fusing instances into different backgrounds. Considering the shortcomings of augmenting images in a limited transformation space of existing traditional data augmentation methods we introduce an object reconstruction enhancement module. The aim of this module is to generate sufficient diversity and non-linear training data at the instance level through a semantic-guided masked autoencoder. In this way the potential of data can be fully exploited in various object detection scenarios. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that the proposed method outperforms baselines by a large margin and achieves new state-of-the-art results under different shot settings.

We propose SNI-SLAM a semantic SLAM system utilizing neural implicit representation that simultaneously performs accurate semantic mapping high-quality surface reconstruction and robust camera tracking. In this system we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition to fully utilize the correlation between multiple attributes of the environment we integrate appearance geometry and semantic features through cross-attention for feature collaboration. This strategy enables a more multifaceted understanding of the environment thereby allowing SNI-SLAM to remain robust even when single attribute is defective. Then we design an internal fusion-based decoder to obtain semantic RGB Truncated Signed Distance Field (TSDF) values from multi-level features for accurate decoding. Furthermore we propose a feature loss to update the scene representation at the feature level. Compared with low-level losses such as RGB loss and depth loss our feature loss is capable of guiding the network optimization on a higher-level. Our SNI-SLAM method demonstrates superior performance over all recent NeRF-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets while also showing excellent capabilities in accurate semantic segmentation and real-time semantic mapping. Codes will be available at https://github.com/IRMVLab/SNI-SLAM.

While AI-generated content has garnered significant attention achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion models for video generation quality the complex model architecture and substantial computational demands for both training and inference create a significant gap between these models and real-world applications. This paper presents SNED a superposition network architecture search method for efficient video diffusion model. Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method. Moreover we propose the supernet training sampling warm-up for fast training optimization. To showcase the flexibility of our method we conduct experiments involving both pixel-space and latent-space video diffusion models. The results demonstrate that our framework consistently produces comparable results across different model options with high efficiency. According to the experiment for the pixel-space video diffusion model we can achieve consistent video generation results simultaneously across 64 x 64 to 256 x 256 resolutions with a large range of model sizes from 640M to 1.6B number of parameters for pixel-space video diffusion models.

Local Interpretable Model-agnostic Explanations (LIME) - a widely used post-ad-hoc model agnostic explainable AI (XAI) technique. It works by training a simple transparent (surrogate) model using random samples drawn around the neighborhood of the instance (image) to be explained (IE). Explanations are then extracted for a black-box model and a given IE using the surrogate model. However the explanations of LIME suffer from inconsistency across different runs for the same model and the same IE. We identify two main types of inconsistencies: variance in the sign and importance ranks of the segments (superpixels). These factors hinder LIME from obtaining consistent explanations. We analyze these inconsistencies and propose a new method Stabilized LIME for Consistent Explanations (SLICE). The proposed method handles the stabilization problem in two aspects: using a novel feature selection technique to eliminate spurious superpixels and an adaptive perturbation technique to generate perturbed images in the neighborhood of IE. Our results demonstrate that the explanations from SLICE exhibit significantly better consistency and fidelity than LIME (and its variant BayLime).

Conventional radar feature extraction faces limitations due to low spatial resolution noise multipath reflection the presence of ghost targets and motion blur. Such limitations can be exacerbated by nonlinear object motion particularly from an ego-centric viewpoint. It becomes evident that to address these challenges the key lies in exploiting temporal feature relation over an extended horizon and enforcing spatial motion consistence for effective association. To this end this paper proposes SIRA (Scalable Inter-frame Relation and Association) with two designs. First inspired by Swin Transformer we introduce extended temporal relation generalizing the existing temporal relation layer from two consecutive frames to multiple inter-frames with temporally regrouped window attention for scalability. Second we propose motion consistency track with the concept of a pseudo-tracklet generated from observational data for better trajectory prediction and subsequent object association. Our approach achieves 58.11 mAP@0.5 for oriented object detection and 47.79 MOTA for multiple object tracking on the Radiate dataset surpassing previous state-of-the-art by a margin of +4.11 mAP@0.5 and +9.94 MOTA respectively.

Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs) they enabled new opportunities in 3D generation. However most generative 3D approaches are object-centric and applying them to editing existing photorealistic scenes is not trivial. We propose SIGNeRF a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. A new generative update strategy ensures 3D consistency across the edited images without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh.

Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images along with predicting textures for unseen areas remains a significant challenge. A key limitation of previous methods is their insufficient prior guidance in transitioning from 2D to 3D and in texture prediction. In response we introduce SIFU (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction) a novel approach combining a Side-view Decoupling Transformer with a 3D Consistent Texture Refinement pipeline. SIFU employs a cross-attention mechanism within the transformer using SMPL-X normals as queries to effectively decouple side-view features in the process of mapping 2D features to 3D. This method not only improves the precision of the 3D models but also their robustness especially when SMPL-X estimates are not perfect. Our texture refinement process leverages text-to-image diffusion-based prior to generate realistic and consistent textures for invisible views. Through extensive experiments SIFU surpasses SOTA methods in both geometry and texture reconstruction showcasing enhanced robustness in complex scenarios and achieving an unprecedented Chamfer and P2S measurement. Our approach extends to practical applications such as 3D printing and scene building demonstrating its broad utility in real-world scenarios.

Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging given the complexity of gigapixel slides. Traditionally MIL interpretability is limited to identifying salient regions deemed pertinent for downstream tasks offering little insight to the end-user (pathologist) regarding the rationale behind these selections. To address this we propose Self-Interpretable MIL (SI-MIL) a method intrinsically designed for interpretability from the very outset. SI-MIL employs a deep MIL framework to guide an interpretable branch grounded on handcrafted pathological features facilitating linear predictions. Beyond identifying salient regions SI-MIL uniquely provides feature-level interpretations rooted in pathological insights for WSIs. Notably SI-MIL with its linear prediction constraints challenges the prevalent myth of an inevitable trade-off between model interpretability and performance demonstrating competitive results compared to state-of-the-art methods on WSI-level prediction tasks across three cancer types. In addition we thoroughly benchmark the local- and global-interpretability of SI-MIL in terms of statistical analysis a domain expert study and desiderata of interpretability namely user-friendliness and faithfulness.

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task empowering users to freely define their class vocabularies of interest during inference. However our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities posing a concern for real-world deployment. To this end we introduce Semantic Hierarchy Nexus (SHiNe) a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class; ii) it integrates these categories into hierarchy-aware sentences; iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities achieving up to +31.9% mAP50 with ground truth hierarchies while retaining improvements using hierarchies generated by large language models. Moreover when applied to open-vocabulary classification on ImageNet-1k SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector without incurring additional computational overhead during inference. The code is open source.

Recently efficient Vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally they use 4x4 patch embeddings and a 4-stage structure at the macro level while utilizing sophisticated attention with multi-head configuration at the micro level. This paper aims to address computational redundancy at all design levels in a memory-efficient manner. We discover that using larger-stride patchify stem not only reduces memory access costs but also achieves competitive performance by leveraging token representations with reduced spatial redundancy from the early stages. Furthermore our preliminary analyses suggest that attention layers in the early stages can be substituted with convolutions and several attention heads in the latter stages are computationally redundant. To handle this we introduce a single-head attention module that inherently prevents head redundancy and simultaneously boosts accuracy by parallelly combining global and local information. Building upon our solutions we introduce SHViT a Single-Head Vision Transformer that obtains the state-of-the-art speed-accuracy tradeoff. For example on ImageNet-1k our SHViT-S4 is 3.3x 8.1x and 2.4x faster than MobileViTv2x1.0 on GPU CPU and iPhone12 mobile device respectively while being 1.3% more accurate. For object detection and instance segmentation on MS COCO using Mask-RCNN head our model achieves performance comparable to FastViT-SA12 while exhibiting 3.8x and 2.0x lower backbone latency on GPU and mobile device respectively.

We present SHINOBI an end-to-end framework for the reconstruction of shape material and illumination from object images captured with varying lighting pose and background. Inverse rendering of an object based on unconstrained image collections is a long-standing challenge in computer vision and graphics and requires a joint optimization over shape radiance and pose. We show that an implicit shape representation based on a multi-resolution hash encoding enables faster and robust shape reconstruction with joint camera alignment optimization that outperforms prior work. Further to enable the editing of illumination and object reflectance (i.e. material) we jointly optimize BRDF and illumination together with the object's shape. Our method is class-agnostic and works on in-the-wild image collections of objects to produce relightable 3D assets for several use cases such as AR/VR movies games etc.

We propose a novel feed-forward 3D editing framework called Shap-Editor. Prior research on editing 3D objects primarily concentrated on editing individual objects by leveraging off-the-shelf 2D image editing networks utilizing a process called 3D distillation which transfers knowledge from the 2D network to the 3D asset. Distillation necessitates at least tens of minutes per asset to attain satisfactory editing results thus it is not very practical. In contrast we ask whether 3D editing can be carried out directly by a feed-forward network eschewing test-time optimization. In particular we hypothesise that this process can be greatly simplified by first encoding 3D objects into a suitable latent space. We validate this hypothesis by building upon the latent space of Shap-E. We demonstrate that direct 3D editing in this space is possible and efficient by learning a feed-forward editor network that only requires approximately one second per edit. Our experiments show that Shap-Editor generalises well to both in-distribution and out-of-distribution 3D assets with different prompts and achieves superior performance compared to methods that carry out test-time optimisation for each edited instance.